Intelligent Database Systems Lab

Presenter : MIN-CONG WUAuthors : PUTERI N.E. NOHUDDIN , FRANS COENEN , ROB CHRISTLEY , CHRISTIAN SETZKORN , YOGESH PATEL , SHANE WILLIAMS C

2012.KBS

Finding ‘‘interesting’’ trends in social networks using frequent pattern mining and self organizing maps

Intelligent Database Systems Lab

Outlines

MotivationObjectivesMethodologyExperimentsConclusionsComments

Intelligent Database Systems Lab

Motivation• Number of trends may be identified, too many

to allow simple inspection by decision makers. Some mechanism was therefore required to allow the simple presentation of trend lines.

Intelligent Database Systems Lab

Objectives

• Generating frequent pattern trends,and use SOM technology a process for assisting the analysis of the identified trends, and to identify ‘‘interesting’’ changes in trends.

Intelligent Database Systems Lab

Methodology-The trend mining mechanism

Intelligent Database Systems Lab

Methodology - Frequent pattern trend mining (TM-TFP)Input: Data set :{t1,t2,..,tn}, ti={a,…,z}a={a1,a2,…,an}, support:3Interestpattern: {a,c,s}Example:support:3Interestpattern: {a,c,s}ID Item set ordered

Intelligent Database Systems Lab

id

m

m

IDConditions Target

Conditions Targettree

Methodology - Frequent pattern trend mining (TM-TFP)

Frequentpattern

{a,b,c,d}={0,0,2500,3311,2718,0,0,0,2779}{a,b,c,e}={3,12,6,0,100,2437,0,56,79}{a,c,e,f}={0,0,0,2568,345,23,90,0,459}

Intelligent Database Systems Lab

Methodology – Trend clusteringInput: v1,v1,..,vnProcess: || V – Wi || = min { || V – Wj || }

Output: BMU

CE/通用格式CE/通用格式

CE/通用格式

CE/通用格式

CE/通用格式CE/通用格式

CE/通用格式

CE/通用格式

Intelligent Database Systems Lab

Methodology – Trend clusters analysis

*

e*k

*

Intelligent Database Systems Lab

Experiment - Cattle movement database

Intelligent Database Systems Lab

Experiment - Cattle movement trend mining

Intelligent Database Systems Lab

Experiment - Deeside Insurance database

Intelligent Database Systems Lab

Experiment - Deeside Insurance trend mining

Intelligent Database Systems Lab

Conclusions

• By employing the SOM clustering technique, the large number of trend lines that are typically identified may be grouped to facilitate a better understanding of the nature of the trends.

Intelligent Database Systems Lab

Comments• Advantages

- a better understanding of the nature of the trends.Applications- self organizing map