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Data Mining Techniques and Applications

Elective-I. Examination Scheme- In semester Assessment: 30 End semester Assessment :70. Data Mining Techniques and Applications. Text Books: Data Mining Concepts and Techniques- Micheline Kamber Introduction to Data Mining with case studies- G.k.Gupta. Reference Books:

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Data Mining Techniques and Applications

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  1. Elective-I Examination Scheme- In semester Assessment: 30 End semester Assessment :70 Data Mining Techniques and Applications Text Books: Data Mining Concepts and Techniques- Micheline Kamber Introduction to Data Mining with case studies-G.k.Gupta Reference Books: Mining the Web Discovering Knowledge from Hypertext data- Saumencharkrobarti Reinforcement and systemic machine learning for decision making- ParagKulkarni

  2. Unit-2) Concepts of frequent patterns,Associations and Correlation.. • Market Basket Analysis • Frequent item set, Closed item set, Association Rules • Mining multilevel Association Rules • Constraint based association rule mining • Apriori Algorithm • FP growth Algorithm

  3. Some Definitions • Itemset: Transaction is a set of items (Itemset). • Confidence: It is the measure of trust worthiness associated with each discovered pattern. • Support : It is the measure of how often the collection of items in an association occur together as percentage of all transactions • Frequent itemset : If an itemset satisfies minimum support,then it is a frequent itemset.

  4. Market Basket Analysis: • Def: Market Basket Analysis (Association Analysis) is a mathematical modeling technique based upon the theory that if you buy a certain group of items, you are likely to buy another group of items. • It is used to analyze the customer purchasing behavior and helps in increasing the sales and maintain inventory by focusing on the point of sale transaction data.

  5. Market Basket Analysis: • identify purchase patterns • what items tend to be purchased together • obvious: steak-potatoes; diaper- baby lotion • what items are purchased sequentially • obvious: house-furniture; car-tires • what items tend to be purchased by season

  6. Continude.. • Categorize customer purchase behavior • purchase profiles • profitability of each purchase profile • Use it for marketing • layout or catalogs • select products for promotion • space allocation

  7. Possible Market Baskets Customer 1: beer, pretzels, potato chips, aspirin Customer 2: diapers, baby lotion, grapefruit juice, baby food, milk Customer 3: soda, potato chips, milk Customer 4: soup, beer, milk, ice cream Customer 5: soda, coffee, milk, bread Customer 6: beer, potato chips

  8. Purchase Profiles beauty conscious kids’ play convenience food health conscious pet lover women’s fashion sports conscious gardener kid’s fashion smoker automotive hobbyist casual drinker photographer student/home office new family tv/stereo enthusiast illness (prescription) illness over-the-counter seasonal/traditional personal care casual reader homemaker home handyman home comfort men’s image conscious fashion footwear sentimental men’s fashion

  9. Purchase Profiles • Beauty conscious • cotton balls • hair dye • cologne • nail polish

  10. Market Basket Analysis • BENEFITS: • simple computations • can be undirected (don’t have to have hypotheses before analysis) • different data forms can be analyzed

  11. Definition: Frequent Itemset • Itemset: • A collection of one or more items • Example: {Milk, Bread, Diaper} • Support count () • Frequency of occurrence of an itemset • E.g. ({Milk, Bread, Diaper}) = 2 • Support • Fraction of transactions that contain an itemset • E.g. s({Milk, Bread, Diaper}) = 2/5 • Frequent Itemset • An itemset whose support is greater than or equal to a minsup threshold

  12. Association Rule Mining • Given a set of transactions, find rules that will predict the occurrence of an item based on the occurrences of other items in the transaction Market-Basket transactions Example of Association Rules {Diaper}  {Beer},{Milk, Bread}  {Eggs,Coke},{Beer, Bread}  {Milk}, Implication means co-occurrence..

  13. Mining Association Rules Example of Rules: {Milk, Diaper}  {Beer} (s=0.4, c=0.67){Milk, Beer}  {Diaper} (s=0.4, c=1.0) {Diaper, Beer}  {Milk} (s=0.4, c=0.67) {Beer}  {Milk, Diaper} (s=0.4, c=0.67) {Diaper}  {Milk, Beer} (s=0.4, c=0.5) {Milk}  {Diaper, Beer} (s=0.4, c=0.5) • Observations: • All the above rules are binary partitions of the same itemset: {Milk, Diaper, Beer} • Rules originating from the same itemset have identical support but can have different confidence • Thus, we may decouple the support and confidence requirements

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