data and knowledge visualization in knowledge discovery process

8/19/2019 Data and Knowledge Visualization in Knowledge Discovery Process

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V i s u a l i z a ti o n S u p p o r t f o r a U s e r - C e n t e r e d K D D P r o c e s s

T u B a o H o

Japan A dvanced Instituteof

Science and Technology

Tatsunokuchi, Ishikawa

923-1292 Japan

81-761-51-1730

bao@ja is t .ac . jp

T r o n g D u n g N g u y e n

Japan A dvanced Institute of


Tatsunokuc hi, Ishikawa

923-1292 Japan

+81-761-51-1732

n g u ye n @ j a i s t . a c . j p

D u n g D u c N g u y e n

Japan Adv anced Instituteof


Tatsunokuchi, Ishikaw a

923-1292 Japan

81-761-51-1732

dungduc@ja is t .ac . jp

ABSTRACT

Viewing knowledge d i s covery as a us er - cen ter ed proces s tha t

r equ i r es an e f f ec t ive co l l abora t ion be tw een the us er and the

d i s covery s ys t em, our work a ims to s uppor t an ac t ive ro l e o f the

us er in tha t p roces s by deve lop ing s ynerg i s t i c v i s ua l i za t ion too l s

in t egra t ed in our d i s covery s ys t em D2M S. Thes e too l s p rov ide an

ab i l i t y o f v i s ua l i z ing the en t i r e p roces s o f kno wledge d i s covery in

order to he lp the us er wi th da ta p reproces s ing , s e l ec t ing min ing

a lgor i thms and parameter s , eva lua t ing and compar ing d i s covered

model s , and t ak ing con t ro l o f t he whole d i s cover p roces s . Our

cas e- s tud ies wi th two medica l da tas e t s on me ning i t i s and s tomac h

cancer s how tha t , wi th v i s ua l i za t ion too ls in D2M S, the us er ga ins

be t t e r i ns igh t i n each s t ep o f the kno wledge d i s co very proces s as

wel l t he r e l a t ions h ip be tw een da ta and d i s covered know ledge .

Keywords

model s e l ec t ion , knowledge d i s covery proces s ,

know ledge v i s ua l i za t ion , t he us er ' s ac t ive ro l e .

d a t a a n d

1 . I N T R O D U C T I O N

T h e p r o c e s s o f knowledge discovery in databases ( K D D ) c a n b e

v iewed inheren t ly cons i s t s o f f ive s teps : (1 ) under s t and ing the

appl i ca t ion dom ain , (2 ) da ta p reproces s ing , (3 ) da ta min ing , (4 )

pos t -p roces s ing , and (5 ) app ly ing d i s covered knowledge , where

each s t ep r equ i r es man y dec i s ions be ing made by the us er [10] .

To f ind impl i c i t bu t po ten t i a l ly us efu l pa t t e rns /mode l s f rom la rge

d a t a b as e s , o n e c a n n o t e x p e c t j u s t t o p u s h a l a rg e a m o u n t o f d a t a

in to a KD D s ys tem wi thou t the us er 's par t i c ipa t ion . In o ther

words , t he KD D proces s can be a l t e rna t ive ly v i ewed as a p roces s

of model s e l ec t ion , i . e . , t ha t o f choos ing by the us er the mos t

in t e r es t ing d i s covered pa t t e rns /model s o r a lgor i thms and the i r

s e t t i ngs fo r ob ta in ing s uch pa t t e rns /model s in a g iv en app l i ca t ion .

M o d e l s e l e c ti o n i n K D D i s a c o m p l i c a t e d h u m a n - c e n t e r e d a n d

dom ain-cen ter ed proces s in which the p ar t i c ipa t ion of the us er

p lays a k ey ro le to the s ucces s .

Vi s ua l i za t ion has p roven i t s e f f ec t ivenes s in exp lora to ry da ta

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ana lys i s [2 ] and a h igh po ten t i a l i n knowledge d i s covery in

da tabas es [3 ] , [ 9 ] . I n th i s paper we in t roduce the knowledge

d i s c o v e ry s y s t e m D 2 M S ( D a t a M i n i n g w i t h M o d e l S e l e c ti o n ) th a t

has two m ain con t r ibu t ions to v i s ua l know ledge d i s covery . F i r s t

a r e i t s e f f i c i en t v i s ua l i zer s fo r l a rge m ul t id ime ns iona l da tabas es ,

d i s covered ru les , h i e r ar ch ica l s t ruc tures as wel l a s ynerg i s t i c

v i s ua l i za t ion of da ta an d know ledge . In par t i cu la r , t he novel

v i s ua l i za t ion t echn ique T2 . 5D (Trees 2 . 5 D imen s ions ) fo r l a rge

h ier ar ch ica l s t ruc tures can be s een as an a l t e rna t ive to power fu l

t echn iques fo r r epres en t ing l a rge h ie r ar ch ica l s t ruc tures s uch as

cone t r ees [14] o r hyperbo l i c t r ees [8 ] . Second i s i t s t i gh t

in t egra t ion of the v i s ua l i zer s wi th func t ions in each s t ep o f the

knowledge d i s covery proces s fo r s uppor t ing the model s e l ec t ion

purpos e .

2 . M O D E L SE L E C T I O N I N

D2MS

Figure 1 s hows a concep tua l a r ch i t ec tu re o f D2M S where D ata

M i n i n g c o m p o n e n t c u r r e n t l y i n c l u d e s a d e c i s i o n t r e e l e a r n i n g

(CAB RO [11] ), an d a ru l e l earn ing (LUP C [6] ) s ubs ys tems .

2 .1 U s e r - c e n t e r e d m o d e l selection

The interes t ingness o f d i s c o v e r e d p a t t e rn s / m o d e l s is c o m m o n l y

charac te r i zed by s evera l c r i t e r i a :

e v ide nc e

i nd ica tes the

s ign i f i cance of a f ind ing meas ured by a s t a t i s ti ca l c r it e r ion ;

r edun danc y amoun t s to the s im i l a r i ty o f a f ind ing wi th r es pec t t o

o ther f ind ings and meas ures to what degree a f ind ing fo l lows

f rom another one ;

usefulness

r e l a t es a f ind ing to the goa l o f the

users ; nov e l t y i n c l u d e s th e d e v i a t i o n f ro m p r i o r k n o w l e d g e o f t h e

us er o r s ys t em; s im pl i c i t y r e f er s to the s yn tac t i ca l complex i ty o f

the p res en ta t ion of a f ind ing , and ge ne ral i t y i s d e t e r m i n e d b y t h e

f r ac t ion of the popula t ion a f ind ing r e f ers to . The in t e r es t ingnes s

can be s een as a func t ion of the above c r i t e r i a , and s t rong ly

d e p e n d s o n t h e u s e r a s w e l l h i s / h e r d o m a i n k n o w l e d g e .

I G raphical sern te rfa c e ~ ~

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i i . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

i Data Da taMin ing

Figure 1: Conceptual architecture o f the system

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The key idea o f our s o lu t ion to mode l s e l ec t ion in D2M S i s to

s uppor t an e f f ec t ive par t i c ipa t ion of the us er in th i s p roces s .

Concre te ly , D2MS f i r s t s uppor t s t he us er in do ing t r i a l s on

com bina t ions o f a lgor i thms an d the i r parameter s e t t ings in o rder

to p roduce compet ing model s , and then i t s uppor t s t he us er in

e v a l u a t in g t h e m q u a n t i t a ti v e l y a n d q u a l i t at i v e l y b y p r o v i d i n g b o t h

per for rnanee met r i cs va lues as we l l as v i s u a l i za t ion of thes e

model s (F igure 2 ) .

2 .2 P l a n a n d p l a n m a n a g e r

T h e m o d e l s e l e c t io n i n D 2 M S m a i n l y i n v o l v e s w i t h t h re e s t e p s o f

da ta p re-proces s ing , da ta m in ing , and pos t -p roces s ing as s how n in

F igure 1 . There a r e th r ee phas es in do ing model s e l ec t ion in

D 2 M S , a n d a l l a r e m a n a g e d b y t h e p l a n m a n a g e m e n t m o d u l e : ( i )

r eg i s t e r ing p lans o f s e l ec t ed a lgor i thms and the i r s e t t i ngs ; ( i i )

execu t ing the p l ans to d i s cover mod el s ; ( i i i) s e l ec t ing appropr i a te

m o d e l s b y a c o m p a r a t iv e e v a l u a t i o n o f c o m p e t i n g m o d e l s . T h e s e

phas es can be d one in t e r ac t ive ly in a l l t h r ee s t eps o f the KD D

process .

The f i r s t phas e i s t o r eg i s t e r p l ans . A

plan

i s an o rdered l i s t o f

a lgor i thms as s oc ia t ed wi th the i r parameter s e t t i ngs tha t can y ie ld

a m o d e l o r a n i n t e r m e d i a te r e s u lt w h e n b e i n g e x e c u t e d . T h e p l a n s

are r epres en ted in a t r ee fo rm ca l l ed

plan tree

w h o s e n o d e s a r e

s e l ec t ed a lgor i thms as s oc ia t ed wi th the i r s e t t i ngs ( the top- l e f t

w i n d o w i n F i g u r e 3) . T h e n o d e s o n a p a t h o f t h e p l a n t re e m u s t

fo l low the o rder o f p r eproces s ing , da ta min ing , and

pos tproces s ing . A p lan may c on ta in s evera l a lgor i thms (nodes ) o f

the p reproces s ing and pos tproces s ing s t eps , f o r example f i l l i ng

m i s s i n g v a l u e s b y n a t u r a l c l u s t e r - b a s e d m e a n - a n d - m o d e t h e n

di s cre t i z ing con t inuous a t t r ibu tes by en t ropy-bas ed a lgor i thm in

p r e p r o c e s s i n g . A p l a n c a n b e e d i t e d i n D 2 M S d u r i n g t h e K D D

process .

The s ec ond phas e i s t o execu te r eg i s t e r ed p lans . Whi l e r eg i s t e ring

g r a d u a l ly a p l a n t h e u s e r c a n r u n a n a l g o r it h m j u s t a f t er a d d i n g i t

t o the p l an , t hen eva lua te i t s r es u l t s before dec id ing whether to

cont inue th i s p l an f rom i t s cur r en t s t age wi th o ther a lgor i thms , o r

to back t r ack and t ry the p l an wi th ano ther a lgor i thm or s e t t i ng .

The us er a l s o can run a p l an a f t e r fu l ly r eg i s t e r ing i t , o r even

reg i s t e r a number o f p l ans then run them a l toge ther . The

in te rmedia t e r es u l t s , t he d i s covered model s and the i r s ummar ies

and expor t ed fo rms wi l l be au tomat i ca l ly c r ea ted and s to red in the

model base

The th i rd phas e i s to s e l ec t appropr i a t e model s by the us er . D2MS

p r o v i d e s a s u m m a r y t a b l e p r e s e n t i n g p e r f o r m a n c e m e t r i c s o f

d i s covered model s accord ing to execu ted p lans ( the bo t tom- r igh t

w i n d o w i n F i g u r e 3 ). H o w e v e r , t h e u s e r c a n e v a l u a t e e a c h m o d e l

in deep by v i s ua l i z ing it , b rows ing i t s s l ruc ture , check ing i t s

r e l a t ions h ip wi th the da tas e t , e t c . ( t he top-middle and top- r igh t

w i n d o w s i n F i g u r e 3 ) T h e u s e r c a n a l so v i s u a l i z e s e ve r a l m o d e l s

s imul t ane ous ly fo r compar ing them. B y ge t t ing ins igh t s in to

compet ing model s , t he us er cer t a in ly can make a be t t e r s e l ec t ion

of mode l s .

2 .3 P r o c e s s v i s u a l i z a t i o n

The p lan manager a l lows the us er to c r ea te , ed i t , and manager

p lans wi th the he lp o f the plan visualizer I n F i g u r e 3 , t h e p l a n

v i s ua l i zer d i s p lays a p l an t r ee in the top- l e f t windo w. A p lan t r ee

i s d i s p layed in the fo rm of a t r ee , where each node r epres en t s a

da tas e t , a model , o r an a lgor i thm. I f t he us er double c l i cks on a

d a t a o r m o d e l n o d e , t h e d a t a o r m o d e l v i s u a l i z a t io n w i l l b e

ac t iva ted to s how the us er cor r es ponding da ta o r model .

Otherwis e i f t he us er double c l i cks on an a lgor i thm node , a d i a log

box wi l l be ac t iva ted fo r the us er to en te r o r change the

parameter s o f tha t a lgor i thm. Therefore , t he p l an v i s ua l i zer can

s erve as a hub fo r the us er to ac t iva te o ther v i s ua l i zer s eas i ly .

An other func t ion of p l an v i s ua l i zer is t o a l low the us er fo l lowin g

the d i s cover p roces s . Dur ing the ru nn in g t ime o f a p l an t r ee , the

f in i s hed par t o f t he p l an t r ee wi l l be changed to g ray co lor , and

c u r r e n tl y r u n n i n g n o d e i s b l i n k i n g . T h e u s e r t h e n c a n e a s i ly

s us pend , con t inue , o r run the p l an t r ee s t ep by s tep .

3 . D A T A A N D M O D E L VISUALIZATION

3 .1 D a t a V i s u a l i z a t i o n

W e have chos e n the para l l e l coord ina tes t echn ique fo r v i s ua l i z ing

2 D t a b u l a r d a ta s e ts d e f i n e d b y n r o w s a n d p c o l u m n s . D 2 M S

improves para l l e l coord ina tes in s evera l ways to ada p t

3.1.1 V iewing or ig inal data

T h e b a s i c i d e a o f v i e w i n g a p - d i m e n s i o n a l d a t a se t b y p a r a l le l

coord ina tes i s t o us e p equa l ly s paced ax es - - wh ich a r e para l l e l to

o n e o f t h e s c r e e n a x e s a n d c o r r e s p o n d t o a t t ri b u t e s a n d t h e e n d s o f

t h e a x e s c o r r e sp o n d t o m i n i m u m a n d m a x i m u m v a l u e s f o r e a c h

d i m e n s i o n - - t o r e p re s e n t e a c h d a t a i n s t a n c e a s a p o l y l i n e t h at

c ros s es each ax i s a t a pos i t ion propor t iona l t o i t s va lue fo r tha t

d imens ion . Th i s v i ew g ives the us er a rough idea about the

d i s t r ibu t ion of da ta o n va lue s o f each a t t r ibu te , i n par t i cu la r t he

co lor s o f d i f f e r en t c l as s es in many eas es can s how c lear ly how

clas s es a r e d if f e r en t f rom each o ther . A n exam ple o f the s tomach

c a n c e r d a t a i s v i s u a l i z e d i n t h e b o t t o m - l e f t w i n d o w i n F i g u r e 4 ,

where the da tas e t i s s hown in the top- l e f t window.

3.1.2 Sum mar iz ing data

This v i ew i s s ign i f i can t as the da tas e t may be very l a rge . The key

idea i s no t t o v i ew or ig ina l da ta po in t s bu t t o v i ew the i r

s ummar ies on para l l e l a t t r ibu tes . As WinViz [9 ] , D2MS us es bar

char t s i n the p l ace o f a t t r ibu te va lues on each ax i s . The bar char t s

i n e a c h a x i s h a v e t h e s a m e h e i g h t ( d e p e n d i n g o n t h e n u m b e r o f

pos s ib le a t t r ibu te va lues ) and d i f f e r en t wid ths tha t s ign i fy the

f r equencies o f a t t ribu te va lues . D2MS a l s o p rov ides in t e r ac t ive ly

c o m m o n s t a t i s t i c s o n e a c h a t t r i b u t e a s m e a n o r m o d e , m e d i a n ,

var i ance , box p lo t s , e t c . The top- r igh t window in F igure 4 s how s

the s umm ar ies o f the s tomach c ancer da ta .

I I

peUng

leb

I SelectApply

A ~ I I h ~ - O ~ a r c ~ s

F i g u r e 2 : A n i l l u s t r a t io n o f u se r - c e n t e r e d m o d e l s e l e c t i o n

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i C - 7 -

~ i ~ i 7 1 ~ i S

- - 7 - ' ~ - ~ 7 7 ~ ° ~ ~ 3 ~ 3 ~ ~ E S . . . .. . ' ~ ; X .; ;: ;' 7~ .~

F i g u r e 3 : A s c r e e n s h o t o f D 2 M S i n s e l e c t i n g m o d e l s l e a r n e d

f r o m W i s c o n s i n b r e a s t c a n c e r d a t a . T h e t o p - l e f t w i n d o w

s h o w s t h e p l a n t r e e ; t h e t o p - m i d d l e w i n d o w s h o w s a t i g h t l y -

c o u p l e d v i e w o f a d e c i s i o n t r e e l e a r n e d b y C A B R O ; t h e t o p -

r i g h t w i n d o w s h o w s a r u l e s e t l e a r n e d b y L U P C ; t h e b o t t o m -

l e f t w i n d o w f o r d i s p l a y i n g i n t e r m e d i a t e c o m p u t a t i o n r e s u l t s

a n d t h e b o t t o m - r i g h t w i n d o w s h o w s t h e s u m m a r y t a b l e o f

p e r f o r m a n c e m e t r i c s o f d i s c o v e r e d m o d e l s .

3 1 3 Querying data

T hi s v i e w se rve s t he hypot he s i s ge ne ra t i on a nd hypot he s i s t e s t i ng

by t he use r . I t a l l ows t he use r t o v i e w subse t s o f t he da ta se t

de t e rmi ne d by que r i e s . T he r e a re t h re e t ype s o f que r i e s : ( i) ba se d

on a va l ue o f the c l a s s a t tr i bu t e whe re t he q ue ry de t e rm i ne s t he

subse t o f a l l i ns t a nc e s be l ong i ng t o t he i nd i c a t e d c l a s s ; ( i i ) ba se d

on a va l ue o f a de sc r i p t i ve a t tr i bu t e whe re t he que ry de t e rmi ne s

t he subse t o f a l l i ns t a nc e s ha v i ng t h i s va l ue , ( i i i ) ba se d on a

c on j unc t i on o f a t tr i bu t e -va l ue s pa ir s whe re t he que r y de t e rmi ne s

t he subse t o f a l l ins t a nc e s s a t i s f i e d t h is c on j unc t i on . T he que r i e s

c a n be de t e rmi ne d by j us t us i ng po i n t -a nd-c l i c k . T he subse t o f

i ns t a nc e s ma t c he d t he que ry i s v i sua l i z e d i n v i e wi ng da t a mode

a n d i n s u m m a r i z i n g d a t a m o d e . T h e g r a y r e g i o n s o n e a c h a x i s

show t he p ropor t i ons o f spe c i f i e d i ns t a nc e s on va l ue s o f t h i s

a t t r i bu t e a s shown i n bo t t om-r i gh t wi ndow i n F i gure 4 ) .

3 . 2 R u l e V i s u a l i z a ti o n

A rule i s a pa t te rn re la ted to severa l a t t r ibute-va lues and a subse t

o f i ns t a nc e s. T he i mp or t a nc e i n v i sua l i z i ng a ru l e is how t h i s l oc a l

s t ruc t u re i s v i e we d i n i t s r e l a t i on t o t he whol e da t a se t , a nd how

t he v i e w suppor t s t he u se r ' s e va l ua t i on on t he ru l e i n t e re s ti ngne s s .

D2MS' s ru l e v i sua l i z e r a l l ows t he use r t o v i sua l i z e ru l e s i n t he

f o r m

a n t e c e d e n t ~ c o n s e q u e n t

w h e r e

a n t e c e d e n t

i s a c on j unc t i on

of a t t r i bu t e -va l ue pa i r s , c o n s e q u e n t i s a c on j unc t i on o f a t t r i bu t e -

va l ue pa i r s i n c a se o f a s soc i a ti on ru l e s , a nd i s a va l ue o f the c l a s s

a t t ri bu t e i n c a se o f p re d i c t i on ru l e s . A ru l e i s s i mpl y d i sp l a ye d by

a subse t o f pa ra l l e l c oord i na t e s i nc l ude d i n a n t e c e d e n t a n d

c o n s e q u e n t . T h e D 2 M S ' s r u l e v i s u a l i z e r h as t h e f o l l o w i n g

f i mc t i ons :

3 . 2 .1 F i e w i n g r u l e s

E a c h ru l e i s d i sp l a ye d by po l y l i ne t ha t goe s t h rough t he a xe s

c on t a i n i ng a t t r i bu t e -va l ue s oc c ur re d on t he a n t e c e de n t pa r t o f the

ru l e l e a d i ng t o t he c onse qu e n t pa r t o f t he ru l e t ha t a re d i sp l a ye d

wi t h d i f f e re n t c o l o r . In t he c a se o f p re d i c t i on ru l e s , t he r a t i o

a s soc i a t e d wi t h e a c h c l a s s i n t he c l a s s a t tr i bu t e c or re sponds t o t he

num be r o f i ns t anc e s o f the c l a s s c ove r e d by t he ru l e ov e r t he to t a l

num be r o f i ns ta nc e s i n t he c l a s s . T h i s v i e w g i ve s a f i r s t

obse rva t i on o f the ru l e qua l i t y .

3 2 2 Viewing rules and data

T he subse t o f i ns t a nc e s c ove r e d by a ru l e i s v i sua l i z e d toge t he r

wi t h t he ru l e by pa ra l l e l c oord i na t e s o r by summa r i e s on pa ra l l e l

c oord i na t e s . F rom t h i s subse t o f i ns t a nc e s , t he use r c a n s e e t he s e t

o f r u l e s e a c h o f t h e m c o v e r s o m e o f t h e se i n s t an c e s , o r t h e u s e r

c a n sm oot h l y c ha n ge t he va l ue s o f a n a t t r i bu te i n t he ru l e t o s e e

o t he r r e l a t e d pos s i b l e ru l e s . T he se pos s i b l e ope ra t i ons f a c i l i t a t e

t he use r i n e va l ua t i ng t he qua l i t y o f th i s ru l e : a ru l e i s good i f

i ns t a nc e s c ove re d by i t a re no t r e c ogn i z e d by o t he r ru l e s , a nd

v i c e -ve r sa . T he ru l e s fo r a c l a s s c a n be d i sp l a ye d t oge t he r , a nd

i ns t a nc e s o f t he c l a s s a s we l l o f o t he r c l a s se s c ove re d by t he se

ru l e a re d i sp l a ye d .

3 . 3 T r e e V i s u a l i z a t io n

D2MS prov i de s s e ve ra l v i sua l i z a t i on t e c hn i que s t ha t a l l ow t he

use r t o v i sua l i z e e f fe c t i ve l y l a rge h i e ra rc h i c a l s t ruc t u re s .

3 3 1 Different modes of viewing hierarchical

structures

D 2 M S t r e e v i s u a l iz e r p r o v i d e s m u l t i p l e -v i e w s o f t r e e s o r

h i e ra rc h i c a l s t ruc tu re s (F i gure 5 ) .

• T ig h t l y - c o u p l e d v i e w s: T h e g l o b a l v i e w s h o w s t h e t r e e

s t ruc t u re wi t h node s i n s a me sma l l s i z e wi t hou t l a be l s a nd

t he re fore i t c a n d i sp l a y a t r e e fu l l y o r a l a rge pa r t o f i t ,

de pe nd i ng on t he t r e e s i z e . T he de t a i l e d v i e w shows t he t r e e

s t ruc t u re a nd node s wi t h t he i r l a be l s a s soc i a t e d wi t h

ope ra t i ons t o d i sp l a y node i n forma t i on . T he g l oba l v i e w i s

a s soc i a t e d wi t h a f i e l d - o f - v i e w o r p a n n e r ( a w i r e - f r a m e b o x )

t ha t c or re sponds t o t he de t a i l e d v i e w [7].

• Cu s to m iz in g v i e w s: In i t i a l l y , a c c ord i ng t o t he use r ' s c ho i c e ,

t he t r e e i s e i t he r d i sp l a ye d fu l l y o r wi t h on l y t he roo t node

a nd i t s d i r e c t sub-node s . T he t r e e t he n c a n be c o l l a p s e d o r

e x p a n d e d pa r t i a l l y o r fu l l y f rom t he roo t o r f rom a ny

i n t e rme di a t e node . Any sub t re e wi t h t he roo t a t a n

unc o l l a pse d c a n be c o l l a pse d i n t o one node . T hus , t he use r i s

a b l e t o i n t e ra c t i ve l y c us t omi z e v i e w s o f t he t r e e to me e t

h i s / he r ne e d a nd i n t e re s t s . A l so , t he use r i s p rov i de d t he

foc us v i e w o n one c l a s s a nd i t s r e l a ti on t o o t he r c l a s se s i n the

who l e h i e ra rc h i c a l s t ruc tu re wi t h d i f f e re n t c o l o r s .

• T i n y m o d e w i t h f i s h - e y e v i e w : Not e t ha t no c ur re n t

v i sua l i z a t i on t e c hn i que a l l ows us t o d i sp l a y e f f i c i e n t l y t he

e n t i r e tr e e whe n i t ha s , s a ys , t e n t housa nds no de s . T he

t i g h t l y - c o u p l e d v i e w s a r e e x t e n d e d w i t h t h r e e v i e w i n g

mode s a c c ord i ng t o t he use r ' s c ho i c e : norma l s i z e , sma l l s i z e

a nd t i ny s i z e . F i sh-e ye i s a n i n t e re s t i ng va r i a n t o f t he c l a s s i c

ove rv i e w-de t a i l b rowse r , p ropos e d i n [4 ]. T h i s v i e w d i s t o r t s

t he ma g ni f i e d i ma g e so t ha t t he c e n t e r o f i n t e re s t is d i sp l a ye d

a t h i gh ma gn i f i c a t i on , a nd t he r e s t o f t he i ma ge i s

p r o g r e s s i v e l y c o m p r e s s e d . I n D 2 M S t r e e v i s u a l i z e r , w e

de f i ne t h re e f i sh -e ye c ompone n t s a s fo l l ows : ( i ) Foc a l po i n t

f : som e node o f c ur re n t i n t e re s t i n t he t r e e; ( i i) D i s t a nc e f rom

foc a l po i n t f t o a nod e x : D ~ x ) = d ~ x ) w h e r e d(x, y)

be t we e n t wo po i n t s x a nd y on t he t r e e is t he numb e r o f l i nks

i n t e rve n i ng on t he pa t h c onne c t i ng t he m i n t he t r e e ; ( i i i )

L e ve l o f de t a i l , i mpor t a nc e , r e so l u t i on : LOD(x) = -d(r ,x)

whe re r i s t he roo t o f t he t r e e.

5 2 1


4/6

3 3 2 Trees 2 5 Dimensions

T 2.5D i s i nsp i re d by t he work o f R e i ngo l d a nd T i l fo rd [13] t ha t

d ra ws t i dy t r e e s i n a r e a sona b l e t i me a nd s t o ra ge . D i f fe re n t f rom

t i gh t l y -c oup l e d a nd f i sh -e ye v i e ws t ha t c a n be s e e n a s l oc a t i on-

ba se d v i e ws (v i e w o f ob j e c ts i n a r e g ion) , T 2 .5D c a n b e s e e n a s a

re l a t i on-ba se d v i e w (v i e w of r e l a t e d ob j e c t s) . T he s t a r t i ng po i n t o f

T 2 .5D i s t he obse rva t i on t ha t a l a rge t r e e c ons i s t s ma ny sub t re e s

t ha t a re no t usua l l y a nd ne c e s sa r i l y v i e we d s i mul t a ne ous l y . T he

ke y i de a o f T 2 .5D i s to r e pre se n t a l a rge t r e e i n a v i r t ua l 3D spa c e

( sub t re e s a re ove r l a ppe d t o r e duc e oc c up i e d spa c e ) whi l e e a c h

sub t re e o f i n t e re s t i s d i sp l a ye d i n a 2D spa c e . T o t h i s e nd , T 2 .5D

de t e rmi ne s t he f i xe d pos i t i on o f e a c h sub t re e ( i t s roo t node ) i n

t wo a xe s X a nd Y , a nd i n a dd i ti on , i t c om put e s dyn a mi c a l l y a Z -

orde r fo r t h i s sub t re e i n a n i ma gi na ry a x i s Z . A sub t re e wi t h a

g i ve n Z -ord e r i s d i sp l a ye d ' a bove i t s s i b l i ngs t hose ha ve h i ghe r

Z -orde r s .

Whe n v i sua l i z i ng a nd na v i ga t i ng a t r e e , a t e a c h mome nt t he Z -

orde r o f a l l node s o n t he pa t h f rom t he roo t t o a n o d e i n f o c u s in

t he t r e e i s s e t t o z e ro by T 2 .SD. T he a c t i v e w i d e p a t h t o a node i n

foc us , whi c h c on t a i ns a l l node s on t he pa t h f rom t he roo t t o t h i s

node i n foc us a nd t he i r s i b l i ngs , i s d i sp l a ye d i n t he f ron t o f t he

sc re e n wi t h h i gh l i gh t e d c o l o r s t o g i ve t he use r a c l e a r v i e w. Ot he r

pa r t s o f the t r e e r e ma i n i n t he ba c kgroun d t o p rov i de a n i ma ge o f

t he ove ra l l s t ruc t u re . Wi t h Z -orde r , T 2 .5D c a n g i ve t he use r a n

i mpre s s i on t ha t t r e e s a re d ra w n i n a 3D spa c e . T he use r c a n e a s i l y

c h a n g e t h e a c t i v e w i d e p a t h b y c h o o s i n g a n o t h e r n o d e i n f o c u s

[12].

We ha ve e xpe r i me nt e d T 2 .5D wi t h va r i ous r e a l a nd a r t i f i c i a l

da t a se ts . I t ha s be e n ve r i f i e d tha t T 2 .5D c a n ha nd l e w e l l tr e e s

wi t h mo re t ha n 20 ,000 node s , ' a nd mo re t ha n 1 ,000 nod e s c a n be

d i sp l a ye d t oge t he r on t he s c re e n [12]. F i gure 7 i l l us t r a te s a p rune d

t re e o f 1795 node s l e a rne d f rom s t oma c h c a nc e r da t a a nd d ra wn

by T 2 .5D (no t e t ha t t he o r i g i na l s c re e n wi t h c o l o r s g i ve s a be t t e r

v i e w t ha n t h i s b l a c k-whi t e s c re e n) .

F i g u r e 4: R u l e v i su a l i z a ti o n in D 2 M S t o p - l e ft w i n d o w s h o w s

t h e l i s t o f d i s c o v e r e d r u l e s t h e m i d d l e - l e f t a n d t h e t o p - r i g ht

w i n d o w s s h o w a r u l e u n d e r i n sp e c t io n a n d b o t t o m w i n d o w

di s p la y s t h e i ns t a nces co v ered by t h a t ru le .

4 . V I S U A L I Z A T I O N I N T H E K D D

P R O C E S S

Fi gure 2 sho ws t ha t , i n D2M S, i n o rd e r t o ta ke t he c on t ro l o f t he

KD D p roc e s s t he: use r ne e ds v i su a l i z a t i on suppor t t o de c i de wha t

t a sk t o do ne x t a nd wha t a re r i gh t a l gor i t hms a nd pa ra me t e r s fo r

t ha t t a sk . . For e xa mpl e , a f t e r e xa mi n i ng t he da t a by da t a

v i sua l i z e r s , t he use r c a n de c i de t ha t da t a r e qu i re d i s c re t i z a t i on o r

no t , a nd i f t he y do w ha t k i nd o f d i s c re ti z a t i on a l gor i t hms c a n be

su i t a b le fo r t ha t da ta . In t h i s s e c t i on we w i l l de sc r i be i n de t a i l how

t h e u s e r u s e s t h e s e v i s u a l i z a t i o n t o o l s t o m a n a g e t h e K D D

process .

4 . 1 T h e w h o l e p r o c e s s v is u a l i z a ti o n w i t h p la n

v i s u a l i z a t i o n

T h e f r a m e w o r k o f D 2 M S a l l o w s m a n y a l g o r it h m s a n d v i s u a l iz e r s

t o work t oge t he r i n a n i n t e gra t e d e nv i ronme nt . T ha t p rov i de s t he

use r a g re a t f l e x i b i l i t y o f c omb i na t i on o f t he se a l gor i t hms a nd

v i sua l i z e r s i n o rde r t o a rc h i ve a be t t e r r e su l t , howe ve r t ha t ma y

a l s o m a k e t a s k s m o r e c o m p l i c a t e d t o d o . W i t h t o o m a n y

a l gor i t hms i nvo l ve d a nd a l o t o f v i e ws a c t i va t e d t o d i sp l a y

d i f fe re n t da t a o r mode l s , i t i s ha rd t o r e me mbe r a nd unde rs t a nd

t he r e l a t i onsh i ps a mong t he m.

T o so l ve t ha t p rob l e m , p l a n v i sua l i z e r i s de s i gne d a s a hub t o

c on t ro l t he se a l gor i t hms a nd v i sua l i z e r s . Whe n t he use r doub l e

c l i c k on a node i n a p l a n t r e e , i f t ha t node de sc r i be s a n a pp l i e d

a l gor i t hm, t he s e l e c t e d s e t o f pa ra me t e r s w i l l be d i sp l a y e d i n a

pa ra me t e r d i a l og box . I f t he node de sc r i be s a da t a se t o r a mo de l ,

c or re spondi ng v i sua l i z e r wi l l be a c t i va t e d t o p rov i de t he use r t he

v i e w s o f t h a t d a t a o r m o d e l . B y f o l l o w i n g t h e r e la t io n s h i p s a m o n g

node s i n t he p l a n t r e e , the use r e a s i l y t r a c k dow n t he r e l a t i onsh i ps

a mong a c t i va t e d v i e ws a nd a pp l i e d a l gor i t hms .

4 . 2 D a t a v i su a l i z a ti o n i n t h e K D D p r o c e s s

W i t h t h e a b o v e t h r e e v i e w s o f d a ta , D 2 M S i n t eg r a t es d a t a

v i sua l i z a t i on i n t o d i f f e re n t KDD s t e ps by d i sp l a y i ng a nd

i n t e ra c t i ve l y c ha ng i ng t he se v i e ws o f da t a a t a ny t i me . In t he f i r s t

s t e p o f c o l l e c t ing da t a a nd fo rmu l a t i ng t he p rob l e m, t he use r c a n

a nd of t e n ne e d t o v i e w t he o r i g i na l da t a se t a nd i t s summ a r i z a t i on .

T he v i sua l a na l ys i s o f c o l l e c t e d da t a ma y he l p t he use r t o i de n t i fy

i mpor t a n t o r r e dunda n t a t t r i bu t e s o r ne w a t t r i bu t e s t o be a dde d .

T he d a t a v i sua l i z a t ion ha s show n t o be s i gn i f i c a n t i n t he da t a

pre proc e s s i ng s t e p tha t c ons i s t s o f func t i ons on da t a c l e a n i ng ,

i n t e gra t i on , t r a ns forma t i on a nd re duc t i on . For e xa mpl e , ma ny

di sc re t i z a ti on a l gor i t hms prov i de a l t e rna t i ve so l u t i on o f d i v i d i ng a

num e r i c a l a t tr i bu t e i n t o i n t e rva l s , a nd t he v i sua l da t a que ry on t he

d i sc re t i z e d a t t r i bu t e a nd t he c l a s s a t t r i bu t e c a n g i ve i ns i gh t s fo r

de c i s i on . T he da t a v i sua l i z a t i on i s a l so ve ry s i gn i f i c a n t i n da t a

mi n i ng s t e p wi t h da t a que ry mode , a nd pa r t i c u l a r l y i n t he

e va l ua t i on s t e p i n i t s syne rg i s t i c c ombi na t i on wi t h ru l e a nd t r e e

v i sua l i z a t i on .

4 .3 M o d e l V i s u a l i z a t io n in t h e K D D p r o c e s s

A mode l t ha t c a n be unde r s t ood i s a mode l t ha t c a n be t rus t e d . A

da t a mi n i ng a l gor i t hm t ha t use s a huma n-unde rs t a nda b l e mode l

c a n b e c h e c k e d e a s i ly b y d o m a i n e x p e r t s, p r o v i d i n g m u c h n e e d e d

se ma nt i c va l i d i t y t o t he mode l . T o t ha t e nd , mode l v i sua l i z a t i on

p r o v i d e s m u c h h e l p t o t h e u s e r .

T he re a re s e ve ra l wa ys t ha t suppor t t he use r i n e va l ua t i ng t he

q u a l it y o f t h e r u l e t o g e t h e r w i t h o t h e r m e a s u r e s u c h a s c o v e r a g e

522


5/6

a nd a c c ura c y o f t he ru l e. For e xa m pl e , t wo ru l e s p re d i c t i ng a

t a rge t c l a s s ha s t he s a me suppor t a nd c onf i de nc e bu t t he one

wrongl y c ove re d more i ns t a nc e s be l ong i ng t o c l a s se s d i f f e re n t

f rom t he t a rge t c l a ss wou l d be c ons i de re d w orse .

F i gure 4 i l l us t r a t e s ru l e v i sua l i z a t i on i n D2MS whe re t he t op- l e f t

a nd bo t t om l e f t w i ndows d i sp l a y a d i s c ove re d ru l e , a nd t he t op-

r i g h t an d b o t t o m r i g h t w i n d o w s s h o w t h e i n s ta n c e s c o v e r e d b y

tha t rule .

T hre e ma i n c r i t e r i a fo r s e l e c t i ng h i e ra rc h i c a l mode l s a re t he i r

s i z e , a c c ura c y a nd unde rs t a nda b i l i t y . T he t r e e s i z e a nd a c c ura c y

c a n be qua n t i t a t i ve l y e va l ua t e d , a mong t he m t he a c c ura c y i s

wi de l y c ons i de re d t o be o f g re a t impor t a nc e . T h e

unde rs t a nda b i l i t y o f t r ee s i s d i f f i c u l t t o be qua n t i f i e d o r me a sure d ,

a nd t he i de a he re i s t o use t r e e v i sua l i z e r t o suppor t t he

unde rs t a nd i ng o f use r s .

I n t h e c u r re n t v e r s i o n o f C A B R O i n D 2 M S , t h e u s e r c a n g e n e r a te

ne w mode l s e a c h i s c ompose d by a n a t t r i bu t e s e l e c t i on me a sure

c hose n f rom t he ga i n - ra t i o , t he g i n i - i nde x [1 ] , Z 2 a nd R -me a sure ;

a p run i ng t e c hn i que f rom e r ro r -c ompl e x i t y , r e duc e d-e r ro r a nd

pe s s i mi s t i c e r ro r [1 ] ; a nd a d i s c re t i z a t i on t e c hn i que f rom t he

e n t ropy-ba se d a nd e r ro r -ba se d t e c hn i que s .

For e a c h mod e l c a nd i da t e, D2M S t re e v i sua l i z e r d i sp l a ys

gra ph i c a l l y t he c or re spondi ng

pr un e d t r e e , i t s s i ze , a nd i t s

p r e d i c t i on e r r or r a t e . I t o f fe r s t he use r a mul t i p l e v i e w o f the se

t r i al s a nd fa c i l it a t e s t he use r t o c om pa re r e su l t s o f t r ia l s i n o rde r t o

ma ke h i s / he r f i na l s e l e c t i on o f t e c hn i que s / mode l s o f i n t e re s t .

D2MS t re e v i sua l i z e r i s use d no t on l y i n i nduc i ng de c i s i on t r e e s

bu t a l so i n c l a s s i fy i ng unknow n ob j e c t s . I t p l a ys t he ro l e o f t he

i n t e r fa c e fo r v i sua l e xp l a na t i on o f t he ma t c h i ng p roc e s s , i n a wa y

s i mi l a r t o t he e xp l a na t i on i n knowl e dge -ba se d sys t e ms . D2MS

t r ee v i s u a l i z e r s u p p o r ts t h r ee m o d e s o f m a t c h i n g a n u n k n o w n

obj e c t a c c ord i ng t o t he wa y t ha t t he unkn own ob j e c t i s de c l a re d .

• T h e w h o l e re c o r d o f t h e u n k n o w n o b j e c t i s r e a d f r o m a

da t a ba se : D2M S d i re c t l y show t he l e a f node t ha t ma t c he s t he

ob j e c t . T he pa t h f rom t he t r e e roo t un t i l tha t l e a f node wi l l be

h i gh l i gh t e d . In forma t i on a c c umul a t e d a l ong t he pa t h c a n be

v i e w e d a t a n y n o d e .

• Va l ue s o f a t t ri bu t e s a re g i ve n by t he use r whe n a nswe r i ng

t he sys t e m que s t i ons : Que s t i ons a bou t a t t r i bu t e s wi l l be

a ske d a c c ord i ng t o t he h i e ra rc h i c a l s t ruc t u re i n a t op-down

m a n n e r f r o m t h e r o o t . F r o m m e n u t h e u s e r w i l l c h o o s e o n e

va l ue i n t he l i s t o f d i s c re t e va l ue s o f t he a t t r i bu t e o r e n t e r a

num e r i c a l va l ue i n c a se o f c on t i nuous a t tr i bu te . Qu e s t i ons

a re a ske d dyna mi c a l l y a c c ord i ng t o t he s t e pwi se r e f i ne me nt

of t he ma t c h i ng p roc e s s .

• T he use r de c l a re s va l ue s o f a t t ri bu t e s he / she know s : T he use r

i s a b l e t o s e l e c t a t t r i bu t e s t ha t he / she wi she s t o que ry on .

T he se a t t r i bu t e s c a n be s e l e c t e d f rom t he a t t r i bu t e l i s t w i t h

c or re spondi ng va l ue s . Onc e t he a t t r i bu t e -va l ue s pa i r s a re

e n t e re d , t he t r e e v i su a l i z e r wi l l l i m i t t he r e g i ons on t he t r e e

t ha t pa r t i a l l y s a t i s fy t he da t a . T he sys t e m wi l l t he n a sk

a dd i t i ona l que s t i ons t o fu l f i l l t he ma t c h .

5 . A C A S E - S T U D Y

T hi s s e c t i on i l l us t r a t e s t he u t i l i t y o f syne rg i s t i c v i sua l i z a t i on o f

d a t a a n d k n o w l e d g e o f D 2 M S i n e x tr a c ti n g k n o w l e d g e f r o m a

s t oma c h c a nc e r da t a se t .

F i g u r e 5 : M u l t i p l e v i e w s o f g e n e r a t e d t r e e s i n D 2 M S t h e t o p

w i n d o w s h o w s t h e T 2 . S D v i e w w h i l e t h e b o t t o m w i n d o w

s h o w s t h e t i g h t l y - c o u p l e d v i e w s o f t h e g e n e r a t e d d e c i s i o n t r e e

f r o m s t o m a c h c a n c e r d a t a .

5 .1 T h e s t o m a c h c a n c e r d a ta s e t

T he s t oma c h c a nc e r da t a se t c o l l e c t e d a t t he Na t i ona l C a nc e r

C e nt e r i n T ok yo dur i ng 1962-1991 i s a ve ry p re c i ous sourc e fo r

t he r e se arc h . I t c on t a i ns da t a o f 7 ,520 pa t i e n ts de sc r i be d

or i g i na l l y by 83 n ume r i c a nd c a t e gor i c a l a t tr i bu t e s o f l oc a t ion ,

c ombi ne d re fe c t i on , p re -ope ra t i ve c ompl i c a t i on , pos t -ope ra t i ve

c ompl i c a t i on , e t c . T he p rob l e m i s t o f i nd p re d i c t i ve a nd

de sc r i p t i ve ru l e s fo r t he c l a s s o f pa t ie n t s wh o d i e d w i t h i n 90 da ys

a f t e r ope ra t i on a mi ds t a to t a l o f 5 c l a s ses de a t h wi t h i n 90 da ys ,

de a t h a f te r 90 da ys , de a t h a f t e r 5 ye a r s , a l i ve , unkn own .

S e v e r a l w e l l - k n o w n d a t a m i n i n g s y s t e m s h a v e b e e n a p p l i e d t o d o

t h i s t a sk . Howe ve r , t he ob t a i ne d re su l t s we re f a r f rom

e xpe c t a t i ons : t he y ha ve l ow suppor t a nd c onf i de nc e , a nd usua l l y

re l a t e to on l y a sma l l pe rc e n t a ge o f pa t i e n ts o f t he t a rge t c l a ss .

5 .2 M i n i n g r u l es w i t h v i s u a l L U P C

T h e D 2 M S ' s v i s u a l i z a t i o n t o o l s a s s o c i a t e d i n L U P C a l l o w u s t o

e xa mi ne t he da t a a nd t o ga i n be t t e r i ns i gh t i n t o c ompl e x da t a

b e f o r e l e a rn i n g . W h i l e t h e v i e w i n g m o d e o f o r ig i n a l d a t a o f fe r s

a n i n t u i t i on a bou t t he d i s t r i bu t i on o f i nd i v i dua l a t t r i bu t e s a nd

i ns t a nc e s , t he summa r i z i ng a nd que ry i ng mode s c a n sugge s t

i r r e gu l a r o r r a re e ve n t s t o be i nve s t i ga t e d , o r t o gu i de whi c h

b i a se s c ou l d be use d t o na r row t he huge s e a rc h spa c e . I t i s

c o m m o n l y k n o w n t h a t p a t i e n t s w h o h a v e s y m p t o m s

l i ve r me t a s t a s i s o f a l l l e ve l s 1 , 2 , o r 3 wi l l c e r t a in l y no t

surv i ve . A l so , s e rosa l i nva s i on = 3 i s a t yp i c a l sym pt om of t he

c l a s s de a t h wi t h i n 90 da ys . Wi t h t he v i sua l i z a t i on t oo l s , we

found se ve ra l unusua l e ve n t s . For e xa mpl e , a mong 2329 pa t i e n t s

i n t he c l a s s a l i ve , 5 o f the m ha ve he a v y me t a s t a s i s o f l e ve l 3,

a nd 1 a nd 8 o f t he m ha v e me t a s t a s i s l e ve l 2 a nd 1 , r e spe c t i ve l y .

M o r e o v e r , t h e q u e r y i n g d a t a a l l o w u s t o v e r i f y s o m e s i g n i f i ca n t

c omb i na t i on o f sympt om s suc h a s l i ve r me t a s ta s i s = 3 , a nd

se rosa l i nva s i on = 3 a s show n i n F i gure 6 .

I t is c o m m o n l y k n o w n t h a t p a t ie n t s c a n n o t s u r v i v e w h e n l i v e r

me t a s t a s i s oc c urs a ggre s s i ve l y . L e a rn i ng me t hods whe n a pp l i e d t o

t h i s da t a se ts o f t e n y i e l d ru l e s fo r t he c l as s de a t h wi t h i n 90 da ys

c on t a i n i ng l i ve r m e t a s t a s i s t ha t a re c ons i de re d a c c e p t a b l e bu t

523


6/6

11¢ 01

l l ~ l O l

I

I 4

F i g u r e 6 : V i s u a l i z a t i o n o f d a t a s u g g e s t e d r a r e e v e n t s t o b e

in ves t igated .

no t u se fu l by dom ain exper ts . Also , these d i scove red ru le s u sua l ly

cove r on ly a subse t o f pa t ien t s o f th i s c la s s . Th is low cove rage

means tha t the re a re pa t ien t s o f the c la s s who a re no t inc luded in

liver metastasis and, therefore , i t is d iff icult to detect them.

Us ing v i sua l in te rac t ive LUPC, we ran d i f fe ren t t r i a l s and

spec i f ied pa rame te rs and cons t ra in t s to f ind on ly ru le s tha t do no t

con ta in the a tt r ibu te l ive r_metas ta s i s and /o r i t s combina t ion

wi th two o the r typ ica l a t t r ibu te s , Pe r i to nea lm e tas ta s i s and

Serosa l_ invas ion? ' Be low i s a ru le wi th accu racy 100%

discove red by L UPC tha t can be seen a s a ra re and i r regu la r even t

in the target c lass .

Rule 8 [accuracy = 1.0 (4/4), cover = 0.001 (416712) ]

IF category = R AND sex = F AND proximal3.hird = 3

A N D mi dd l eJ : h i rd = 1

T H EN c las s = dea th w i t h i n 90 day s

R E F E R E N C E S

[1] Breiman, L. , Friedman, J . , Olshen, R. , and Stone, C. ,

Classi f icat ion an d Regress ion Trees ,

Belmon t , CA:

Wadsworth , 1984.

[2 ] Ca rd , S . K . , Mack in lay , J. D . , Shne ide rman , B . ,

Readings in

Information Visualizat ion,

Morg an Kaufmann , 1999 .

[3 ] Fayya d , U.M. , Gr in s te in . G .G. , and W ie rse , A . ,

Information

Visual i za t ion in D ata M ining and Knowle dge D isc ov e ry ,

Morgan K aufmann , 2002 .

[ 4 ] F u r n as , G . W . , T h e F I S H E Y E V i e w : A N e w L o o k a t

S t ruc tu red F i le s ,

Bell Laborator ies Technical

M e m orandum ,

#81-11221-9, 1981.

[5 ] Han , J. and Cercone , N . , Ru leViz : A Mode l fo r Visua l iz ing

Know ledge D iscove ry P rocess ,

Six th In ter . Co nf . on

Kno wle dg e D isc ov ery a nd D ata M ining ,

2000, pp. 244-253.

[6 ] Ho , T .B. , Nguyen , D.D. , and Kawasak i , S. , Min ing

Pred ic t ion Ru les f rom Minor i ty Classes ,

In te r W ork shop

Rule -Base dD ata M ining ,

Tok yo, 2001. pp. 254-264.

[7 ] Kumar , H . P . , P la i san t , C . , Shne iderman , B . , Brows ing

Hie ra rch ica l Da ta w i th Mul t i -Leve l Dynamic Q uer ie s and

Prun ing ,

In te r Journa l o f Hum an-Com pute r S tud ies ,

46(1),

pp. 103-124, 1997.

The p red ic t ion o f ra re even ts i s becoming pa r t i cu la r ly inte re s ting.

W hen suppos ing tha t some a t t r ibu te -va lue pa i r s may cha rac te r ize

some ra re and /o r s ign i f ican t even ts . LUPC, thanks to i t s

a ssoc ia ted v i sua l iza t ion too ls , a l lows us to examine e f fec t ive ly the

hypo thes i s space and iden t i fy ra re ru le s wi th any g iven sma l l

support or confidence. An e xam ple is to f ind rules in the c lass

a l ive tha t con ta in the symptom l ive r_metas ta s i s . Such even ts

a re ce r ta in ly ra re and in f luence human dec is ion mak ing . W e

found ra re even ts in the c la s s a l ive , such a s ma le pa t ien t s

ge t t ing l ive r me tas ta s i s a t se r ious leve l 3 can su rv ive wi th the

accu racy o f 50%.

Rule i [accuracy= 0.500 2/4); cover = 0.001 4/6712)]

IF sex = M AND type = B1 AND liver_metastasis = 3

AND middle third = 1

THEN dass = alive

6 . C O N C L U S I O N

W e have p re sen ted the knowledge d i scove ry sys tem D2MS wi th

suppor t fo r mode l se lec t ion in teg ra ted wi th v i sua l iza t ion . W e

emphas ize the c ruc ia l ro le o f the u se r ' s pa r t i c ipa t ion in the m ode l

se lec t ion p rocess o f knowledge d i scove ry and have deve loped

data , ru le and tree visualizers in D2MS to support such

pa r t ic ipa tion . Our bas ic idea i s u se r igh t v i sua l iza t ion techn iques

in r igh t p laces~ and v i sua l iza t ion shou ld b e in teg ra ted in to the

s teps o f the know ledge d i scove ry p rocess . D2MS wi th i t s

v i sua l iza t ion suppor t has been used and shown advan tages in

ex t rac t ing knowledge f rom a rea l -wor ld app l ica t ion on s tomach

cancer data .

[8 ] Lamping , J . and Rao , R . , The Hyperbo l ic Browse r : A Focus

+ Con tex t Techn iques fo r Visua l iz ing La rge H ie ra rch ie s ,

Journal o f V i sual Language s and Com put ing ,

7(1), pp. 33-

55, 1997.

[9 ] Lee , H .Y. , Ong , H.L. , and Quek , L .H. , Exp lo i t ing

Visua l iza t ion in Knowledge Discove ry ,

Firs t Inter . Conf. on

Kno wle dge D isc ove ry an d D ata M ining ,

1995, pp. 198-203.

[10 ] Mann i la , H . , Me thods and P rob lems in Da ta Min ing ,

Inter.

Conf. on Database Theory ,

Springer, 1997, pp. 41-55.

[ 11 ] Nguyen , T .D. and Ho , T .B. , An In te rac t ive Graph ic Sys tem

fo r Dec is ion Tree Induc t ion ,

J o u r n a l o f J a p a n e s e S o c i e ty

fo r Art i f ic ial Inte l l igence ,

Vo l. 14, N. 1, pp. 131-138, 1999.

[12 ]N guyen , T .D. , Ho , T .B. , and Sh imoda i ra , H . , A

Visua l iza t ion Too l fo r In te rac t ive Lea rn ing o f La rge

Dec is ion Trees ,

Twe l~h IEEE ln te r. Con f . on Tool s w i th

Artificial Intelligence,

2000, pp. 28-35.

[13 ]R e ingo ld , E .M. and Ti l fo rd , J .S ., T id ie r Drawings o f

Trees ,

IEEE Transact ions on Sof tware Engineering,

Vol .

SE-7, No. 2, pp. 223-228, 1991.

[ 1 4 ] R o b e r t s o n , G . G . , M a e k i n l a y , J . D . , a n d C a r d , S . K . ,

C o n e T r e e s : A n i m a t e d 3 D V i s u a l i z a ti o n o f

H i e r a r c h i c a l I n f o r m a t i o n ,

A C M C o n f . o n H u m a n

F a c t o r s i n C o m p u t i n g S y s t e m s ,

1 9 9 1 , p p . 1 8 9 - 1 9 4 .

5 2 4

data and knowledge visualization in knowledge discovery process

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