data mining (student presentation) samira roshan_asma akbari mehr 87-88
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Data MiningData Mining(Student Presentation)(Student Presentation)
Samira Roshan_Asma AkbariSamira Roshan_Asma Akbari
Mehr 87-88Mehr 87-88
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• 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
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• Data collected and stored at enormous speeds (GB/hour)
• Traditional techniques infeasible for raw data
• Data mining may help scientists
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DATA Base
Target Data
Transformed Data
Patternsand
Rules
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Classification
Regression
Collaborative Filtering
Clustering
Association rules
Deviation detection
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Classifier Decision rules
Salary > 5 L
Prof. = Exec
New applicant’s data
Many approaches: Statistics, Decision Trees,Neural Networks, ...
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Unsupervised learning when old data with class labels not available e.g. when introducing a new product.
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Given set T of groups of itemsExample: set of item sets purchased
...
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The use of data, particularly about people, for datamining has serious ethical implications.
When applied to people discriminate.
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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?
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InstancesInstances: the individual, independent examples of a concept
AttributesAttributes: measuring aspects of an instanceWe will focus on nominal and numeric ones
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number of nuclei (values: 1,2)number of tails (values: 1,2)color (values: light, dark)wall (values: thin, thick)
LethargiaBurpomaHealthy
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# ColorLightDark
Lethargia
32
Burpoma
12
Healthy22
# Tails12
Lethargia
50
Burpoma
03
Healthy22
# Nucleus
12
Lethargia
41
Burpoma
03
Healthy22
# Membrance
ThinThick
Lethargia32
Burpoma21
Healthy31
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#ColorLightDark
Lethargia
32
Burpoma
12
Healthy22
#Tails12
Lethargia
50
Burpoma
03
Healthy22
#Nucleus
12
Lethargia
41
Burpoma
03
Healthy22
#Membrance
ThinThick
Lethargia32
Burpoma21
Healthy31
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Tails
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#ColorLightDark
Lethargia
32
Burpoma
00
Healthy02
#Nucleus
12
Lethargia
41
Burpoma
00
Healthy02
#Membrance
ThinThick
Lethargia32
Burpoma00
Healthy02
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Tails
Nucleus
Lethargia
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Tails
Nucleus
Lethargia Color
Nucleus
Healthy Burpoma
Lethargia Healthy
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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
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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