Elective-I Examination Scheme- In semester Assessment: 30 End semester Assessment :70 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- Saumen charkrobarti Reinforcement and systemic machine learning for decision making- Parag Kulkarni

 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

 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.

 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.

  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

 Categorize customer purchase behavior  purchase profiles  profitability of each purchase profile  Use it for marketing ▪ layout or catalogs ▪ select products for promotion ▪ space allocation

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

beauty consciouskids’ playconvenience food health consciouspet loverwomen’s fashion sports consciousgardenerkid’s fashion smokerautomotivehobbyist casual drinkerphotographerstudent/home office new familytv/stereo enthusiastillness (prescription) illness over-the-counterseasonal/traditionalpersonal care casual readerhomemaker home handymanhome comfort men’s image consciousfashion footwear sentimentalmen’s fashion

 Beauty conscious  cotton balls  hair dye  cologne  nail polish

 BENEFITS:  simple computations  can be undirected (don’t have to have hypotheses before analysis)  different data forms can be analyzed

 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

 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..

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