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Clustering in Data Warehouse Department of CE MSPVL Polytechnic College Pavoorchatram 1
Overview ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Part 1: Data Mining
Relationship to data warehouse ,[object Object],[object Object],[object Object],[object Object]
Define Data Mining ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Part 2: Association Rules
Association Rules ,[object Object]
Why Association Rules? Bread ,milk Milk ,sugar Pen ,ink
The general form of association rule is ,[object Object],[object Object],[object Object],[object Object]
Consider the Purchase Table ,[object Object],[object Object],[object Object]
Association rules measures ,[object Object],[object Object]
Support ,[object Object],[object Object]
Confidence ,[object Object],[object Object],[object Object],[object Object]
Part 3: classification Classification rules Decision trees Mathematical formula Neural network
Some basic operations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Classification ,[object Object],Age Salary Profession Location Customer type Previous customers Classifier Decision rules Salary > 5 L Prof. = Exec New applicant’s data Good/ bad
Classification ,[object Object]
Why Data Mining ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Classification ,[object Object],Training Data Classification algorithm Classification Rules If age=“31 …. 40” And income=high Then rating = good. Name Age Income Rating abc 20 low fair xyz 31…40 Medium Good mny 40…50 High Excellent
classification ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Classification methods ,[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],Decision trees Salary < 1 M Prof = teacher Age < 30 Good Bad Bad Good
Pros and Cons of decision trees ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Neural network ,[object Object],Hidden nodes Output nodes x1 x2 x3 x1 x2 x3 w1 w2 w3 Basic NN unit A more typical NN
Pros and Cons of Neural Network ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Conclusion: Use neural nets only if decision trees/NN fail. classification
Part 4:Clustering Partitioning clustering algorithm Hierarchical clustering algorithm
Clustering ,[object Object],[object Object],[object Object]
clustering
Similarity
Prevalent  Interesting ,[object Object],[object Object],[object Object],1995 Milk and cereal sell together! Milk and cereal sell together! 1998 Zzzz...
Clustering Algorithm ,[object Object],[object Object]
Partition clustering Algorithm ,[object Object],[object Object]
Hierarchical clustering algorithm ,[object Object],[object Object],[object Object],[object Object]
Part 6: Approaches to data mining problems Discovery of sequential Discovery of patterns in time series Discovery of classification rules Regression
Discovery of sequential patterns Suppose a customer visit the shop three times and purchase the following sequence of item sets. { milk, bread, juice } { bread, eggs } { cookies, milk, coffee } The problem of discovering sequential patterns is to find all subsequences from the given sets of sequences that have a user defined minimum support. Trans_id Time Item_Purchased 101 6.35 Milk, bread, juice 792 7.38 Milk, juice 1130 8.05 Milk, eggs 1735 8.40 Bread, cookies ,coffee
Discovery of patterns in time series ,[object Object],[object Object],[object Object],[object Object]
Discovery of classification rules ,[object Object]
Example ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Regression ,[object Object]
Example ,[object Object],[object Object],[object Object],[object Object],[object Object]
MSPVL Polytechnic college

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Cluster2

  • 1. Clustering in Data Warehouse Department of CE MSPVL Polytechnic College Pavoorchatram 1
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  • 3. Part 1: Data Mining
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  • 8. Why Association Rules? Bread ,milk Milk ,sugar Pen ,ink
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  • 14. Part 3: classification Classification rules Decision trees Mathematical formula Neural network
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  • 26. Part 4:Clustering Partitioning clustering algorithm Hierarchical clustering algorithm
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  • 34. Part 6: Approaches to data mining problems Discovery of sequential Discovery of patterns in time series Discovery of classification rules Regression
  • 35. Discovery of sequential patterns Suppose a customer visit the shop three times and purchase the following sequence of item sets. { milk, bread, juice } { bread, eggs } { cookies, milk, coffee } The problem of discovering sequential patterns is to find all subsequences from the given sets of sequences that have a user defined minimum support. Trans_id Time Item_Purchased 101 6.35 Milk, bread, juice 792 7.38 Milk, juice 1130 8.05 Milk, eggs 1735 8.40 Bread, cookies ,coffee
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Editor's Notes

  1. Each topic is a talk..