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60-520:Seminar

60-520:Seminar . Market Basket Analysis: An Approach to Association Rule Mining in Multiple Store Environment. _______________________________________. Presented By Robyat Hossain University of Windsor February 10, 2006.

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60-520:Seminar

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  1. 60-520:Seminar Market Basket Analysis: An Approach to Association Rule Mining in Multiple Store Environment._______________________________________ Presented By Robyat Hossain University of Windsor February 10, 2006

  2. Market Basket Analysis:An Approach to Association Rule Mining in Multiple Store Environment.______________________________________________________________ Agenda • Introduction • General concept • Discussion on proposed ideas • Conclusion

  3. Market Basket Analysis:An Approach to Association Rule Mining in Multiple Store Environment.______________________________________________________________ • Introduction • MBA • Purpose Peoples attitude • Benefit • Inside of store environment layout changes • Outside of store product bundling direct marketing • Outside of realm of marketing stock inventory operation strategis

  4. Market Basket Analysis:An Approach to Association Rule Mining in Multiple Store Environment.___________________________________________________ • General Concept • The approach • Methods, Measures, Results. • Literature overview on Traditional Association rules.

  5. Market Basket AnalysisGeneral Concept: methods_____________________________ • Method: Transaction 1: Frozen pizza, cola, milk Transaction 2: Milk, potato chips Transaction 3: Cola, frozen pizza Transaction 4: Milk, pretzels Transaction 5: Cola, pretzels Hints that frozen pizza and cola may sell well together, and should be placed side-by-side in the convenience store.. • Results: • we could derive the association rules: • If a customer purchases Frozen Pizza, then they will probably purchase Cola. • If a customer purchases Cola, then they will probably purchase Frozen Pizza.

  6. Market Basket AnalysisGeneral Concept: Measures_____________________________ • Measures: • Suport Suport = (containing the item combination) /( total number of record.) Let the rule Is "If a customer purchases Cola, then they will purchase Frozen Pizza" The support for this = 2 (number of transaction that include both Cola and Frozen Pizza is) / 5(total records ) = 40%. • Confidence: Confidence of a rule = the support for the combination / the support for the condition. For the rule "If a customer purchases Milk, then they will purchase Potato Chips" confidence = support for the combination (Potato Chips + Milk) is 20%/ support for the condition (Milk) is 60%, =33% Suport and confidence are used to select the association rules.

  7. Market Basket AnalysisDiscussion on proposed ideas_______________________________________________________ • Problem Definition • Assumptions • Four Definitions ( Definition 1,2,3,4) • Algorithm • General concepts developing the algorithm • Discussion on several key steps • Performance Evaluation • Data generation • Performance measure • Simulation study

  8. Market Basket AnalysisProposed ideas :Problem Definition_____________________________________________________ Assumptions: D = a database , contains transactional records from multiple stores over time period T Let I={11, 12,. . ., Ir } be the set of product items in D where Ik (1 ≤ k ≤ r ) is the identifier for the kth item. Let X be a set of items in I. We refer X as a k itemset if |X| = k. denote the set of transactions S(subset of I) in D, contain itemset X. Let {T1, T2,. . ., Tm} be the set of mutually disjoint time intervals (periods) Let P={P1, P2,. . ., Pq} set of stores, where Pj (1 ≤ j≤ q) denotes the jth store in the store chain Definition 1.The support of X, denoted by sup(X, D), is the fraction of transactions containing X in database D; i.e., sup(X, D) = |W(X, D)|/|D|. For a specified support threshold σs, X is a frequent itemset if sup(X, D)≥σs. Definition 2.Let X be an itemset in I with context VX, and the subset of transactions in D whose timestamps t and store identifiers p satisfy VX. We define the relative support of X w.r.t the context VX, denoted by For a given threshold for relative support σr, if a frequent itemset X satisfies , we call X a relative-frequent (RF) itemset.

  9. Market Basket AnalysisProblem Definition_____________________________________________________ Let and be the sets of the stores and times that item Ik is sold, V Ik = S k X R k as the context of item Ik;(combination of stores & time where sold) the context of itemset X , consists of two items Ik and Ik’ denoted by VX = V Ik∩ V Ik’ .(common stores & time shared by items in x) Definition 3.Consider two itemsets X and Y. The relative support of X with respect to the context , denoted by is defined as . The confidence of rule X => Y, denoted by conf (X => Y), is defined as Definition 4.Let Z be an RF itemset, where Z = XuY, and . Given a confidence threshold σc, if conf(X => Y)≥ σc, we call X => Y a store-chain (SC) association rule, and Vxuy as the context of the rule.

  10. Market Basket AnalysisAlgorithm_____________________________________________________

  11. Market Basket AnalysisAlgorithm: General concepts___________________________ • We use RFk ,Fk, Ck to denote the set of all relative-frequent k-itemsets , frequent k-itemsets ,candidate k-itemsets. • As the first step of the algorithm, we build a table, called the PT table, • In the first phase, we scan the database for the first time and build a two-dimensional table, called the TS table. The entries denoted by TS(Ti, Pj) , records the number of transactions that occur at store Pj in period Ti. • In the kth phase of the algorithm, we first derive Ck , and, then, generate Fk by evaluating their supports, through scanning the database and removing all infrequent itemsets and also generate RFk from Fk. • Using this TS table and the PT table for a given itemset X, we can determine the | |.

  12. Market Basket AnalysisAlgorithm :Key steps_______________________________ • Methods of • Building the PT(Store-Time) table • Building of TS (transaction)table • Finding of RFk (Relative –frequent itemset) • Generating the store-chain association rule

  13. Market Basket AnalysisAlgorithm: Building the PT table_____________________________________________________ • Hold time & store information for each item • PT table from bit matrices for individual items • Bit Matrices for Items I1 I2 I3

  14. Market Basket AnalysisAlgorithm: Building the PT table_____________________________________________________The method to compute the jth row of PT table for Itemset X

  15. Market Basket AnalysisAlgorithm: Building the TS table_____________________________________________________ • The TS table • At first phase of the algorithm , build the TS table, • The entries denoted by TS(Ti, Pj) , records the number of transactions that occur at store Pj in period Ti. • Using the TS and PT tables for itemset X, we can determine the value | | by summing all the values in the entries of the TS table The process of constructing the table is described in lines 2 through 4 in algorithm

  16. Market Basket AnalysisAlgorithm: Computing RFk ,Fk_____________________________________________________ Here is an example of the computation process: • Suppose ,15 periods( T1 to T15), #of transactions are 19, 17, 14, 25, 20, 17, 15, 27, 21, 20, 22, 18, 25, 21, and 19, • selling periods of product A ,T1 to T10, and involved in 60 transactions • selling periods of product B ,T6 to T15, and involved in 80 transactions • 50 transactions containing both A and B, and sold in periods T6 to T10. • To compute supports and relative supports for itemsets {A}, {B}, and {A, B}, = 60, = 80, and = 50 and |D| = 300, • sup({A}, D) = 60/300 = 0.2, sup({B}, D) = 80/300 = 0.267, and sup({A, B}, D) = 50/ 300 = 0.167. • Bases for relative support are | | = 195, | |=205, and | |= 100, • = 60/195 = 0.308, = 80/205 = 0.39, = 50/100 = 0.5 • Suppose we set rs at 0.1 and rr at 0.35. Then, {A}, {B}, and {A, B} are frequent. • {A} is not relative-frequent, but {B} and {A, B} are relative-frequent.

  17. Market Basket AnalysisPerformance Evaluation: Data______________________________________________ • Data generation Dij is number of transaction of store i & period j Ni is number of products in store i

  18. Market Basket AnalysisPerformance Evaluation: Measures_____________________________ • Performance measures Three measures (errors type A,B,C) for assessingthe magnitudes of the deviations in support, confidence, and the number of association rules when we use the traditional association rules for the store-chain data. Type A error measures the relative difference in the support levels = =(.03 - .02 )/.03 = 33.33% Type B is used to compare the difference in confidence levels = conf(X=>Y) - conf V( X=>Y ))/conf (X=>Y), where conf V(X=>Y) is the rule confidence computed by the traditional methods. The type C error is used to compare the relative difference in the numbers of rules generated by the two methods.

  19. Market Basket Analysis Performance Evaluation: Simulation_____________________________ Simulation Study • The effect of number of stores & periods • The effect of stores size • The effect of different product replacement rate • Summarization

  20. Market Basket AnalysisPerformance Evaluation: Simulation_____________________________ The table Data_Set_Table used in different case for simulation study Data_Set_Table: Data set#of stores#of periodsRange of store sizesProduct replacement rate 1 5 5 50–100 0.001 2 10 10 50–100 0.001 3 50 50 50–100 0.001 4 50 50 10–100 0.001 5 50 50 50–100 0.001 6 50 50 90–100 0.001 7 50 50 50–100 0.001 8 50 50 50–100 0.005 9 50 50 50–100 0.010

  21. Market Basket AnalysisPerformance Evaluation: Simulation_____________________________ Effect analysis for # of stores & periods on type A,B,C error based on data sets 1,2,3 in Data-set_Table

  22. Market Basket AnalysisPerformance Evaluation: Simulation_____________________________ • Effect analysis for store size based on data sets 4,5,6 in Data-set_Table

  23. Market Basket AnalysisPerformance Evaluation: Simulation_____________________________ • Effect analysis for product replacement ratio based on data sets 7,8,9 in Data-set_Table

  24. Market Basket AnalysisPerformance Evaluation: Simulation_____________________________ • The traditional association rules may not be able to extract all important purchasing patterns for a multistore chain, when there are large numbers of stores and periods, a large variation in store sizes, and high product replacement ratios. • we find that the proposed algorithm requires larger process times, but the differences are not substantial. This result is reasonable, because the proposed algorithm requires one more scan of the data than does the Apriori algorithm, and also requires additional basic operations in each phase of the algorithm.

  25. Market Basket Analysis Conclusion_____________________________ • Traditional Associations rule mining was introduced in 1993 by Agrawal et al. fail in multi store environment. • To overcome this, store-chain association rule is proposed where store and period are involved. • A simulation is used to compare the proposed and traditional methods. we have seen that algorithm is computationally efficient. • The outcomes would be used for general or local marketing strategies. Moreover for product procurement , inventory, distribution strategies for store-chain. • This is the promising research area in data mining ,lot of work is left to do like generating store-chain association rules accurately and efficiently, to extend this algorithm for multiple levels in distributed environment. environment

  26. Market Basket AnalysisRefferences_____________________________________ • [1] R. Agrawal, R. Srikant, Fast algorithms for mining association rules, Proceedings of the 20th VLDB Conference, Santiago, Chile, 1994, pp. 478–499. • [2] J.M. Ale, G.H. Rossi, An approach to discovering temporal association rules, Proceedings of the 2000 ACM Symposium on Applied Computing (Vol. 1), Villa Olmo, Como, Italy, 2000, pp. 294– 300. • [3]yen-Liang Chen, Kwei tang, Ren-jie Shen and Ya-Han Hu, “market basket analysis in a multiple store environment” Decision support System, 40(2)(2005) 339-354 • [4] H. Lu, L. Feng, J. Han, Beyond intra-transaction association analysis: mining multi-dimensional inter-transaction association rules, ACM Transactions on Information Systems 18 (4) (2000) 423– 454. • [5] J.F. Roddick, M. Spiliopoilou, A survey of temporal knowledge discovery paradigms and methods, IEEE Transactions on Knowledge and Data Engineering 14 (2002) 750–767. • [6] E. Clementini, P.D. Felice, K. Koperski, Mining multiplelevel spatial association rules for objects with a broad boundary, Data and Knowledge Engineering 34 (3) (2000) 251– 270.

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