Data MiningData Mining(Student Presentation)(Student Presentation)

Samira Roshan_Asma AkbariSamira Roshan_Asma Akbari

Mehr 87-88Mehr 87-88

• There is often information hiddenhidden in the data that is not readily evident

• Human analysts may take weeks to discover useful information

• Much of the data is never analyzed at all

Number of analysts

Total new disk (TB) since 1995

The DataThe Data GapGap

• Data collected and stored at enormous speeds (GB/hour)

• Traditional techniques infeasible for raw data

• Data mining may help scientists

DATA Base

Target Data

Transformed Data

Patternsand

Rules

Classification

Regression

Collaborative Filtering

Clustering

Association rules

Deviation detection

Classifier Decision rules

Salary > 5 L

Prof. = Exec

New applicant’s data

Many approaches: Statistics, Decision Trees,Neural Networks, ...

Unsupervised learning when old data with class labels not available e.g. when introducing a new product.

Given set T of groups of itemsExample: set of item sets purchased

...

The use of data, particularly about people, for datamining has serious ethical implications.

When applied to people discriminate.

Data mining (or simple analysis) on people may comewith a profile that would raise controversial issues of– Discrimination– Privacy– Security

Examples:– Should males between 18 and 35 from countries that

produced terrorists be singled out for search before flight?

– Can people be denied mortgage based on age, sex, race?– Women live longer. Should they pay less for life insurance?

InstancesInstances: the individual, independent examples of a concept

AttributesAttributes: measuring aspects of an instanceWe will focus on nominal and numeric ones

number of nuclei (values: 1,2)number of tails (values: 1,2)color (values: light, dark)wall (values: thin, thick)

LethargiaBurpomaHealthy

# ColorLightDark

Lethargia

32

Burpoma

12

Healthy22

# Tails12

Lethargia

50

Burpoma

03

Healthy22

# Nucleus

12

Lethargia

41

Burpoma

03

Healthy22

# Membrance

ThinThick

Lethargia32

Burpoma21

Healthy31

#ColorLightDark

Lethargia

32

Burpoma

12

Healthy22

#Tails12

Lethargia

50

Burpoma

03

Healthy22

#Nucleus

12

Lethargia

41

Burpoma

03

Healthy22

#Membrance

ThinThick

Lethargia32

Burpoma21

Healthy31

Tails

#ColorLightDark

Lethargia

32

Burpoma

00

Healthy02

#Nucleus

12

Lethargia

41

Burpoma

00

Healthy02

#Membrance

ThinThick

Lethargia32

Burpoma00

Healthy02

Tails

Nucleus

Lethargia

Tails

Nucleus

Lethargia Color

Nucleus

Healthy Burpoma

Lethargia Healthy

If # Tails = 1 then If # Nucleus = 1 then class = Lethargia else If color = light then class = Lethargia else class = Healthyelse If # Nucleus = 1 then class = Healthy else class = Burpom

Resources

• http://office.microsoft.com/

• http://www.wisegeek.com/what-is-a-relational-database.htm

• http://www.cs.toronto.edu/avaisman/cscd34summer/ccsc343s.htm

• www.cl.cam.ac.uk/Teaching/current/Databases/• www.cs.uh.edu/~ceick/6340/dw-olap.ppt