Advanced Data Mining Techniques by David L. Olson Dr., Dursun Delen Dr. (auth.)

By David L. Olson Dr., Dursun Delen Dr. (auth.)

This e-book covers the elemental strategies of information mining, to illustrate the possibility of amassing huge units of information, and studying those information units to realize precious company realizing. The e-book is equipped in 3 elements. half I introduces recommendations. half II describes and demonstrates easy facts mining algorithms. It additionally includes chapters on a few various options frequently utilized in facts mining. half III focusses on enterprise purposes of information mining. tools are awarded with basic examples, purposes are reviewed, and relativ benefits are evaluated.

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Market basket data in its rawest form would be the transactional list of purchases by customer, indicating only the items purchased together (with their prices). This data is challenging because of a number of characteristics:19 x A very large number of records (often millions of transactions per day) x Sparseness (each market basket contains only a small portion of items carried) x Heterogeneity (those with different tastes tend to purchase a specific subset of items). The aim of market-basket analysis is to identify what products tend to be purchased together.

18 Key measures in association rule mining include support and confidence. Support refers to the degree to which a relationship appears in the data. Confidence relates to the probability that if a precedent occurs, a consequence will occur. The rule X o Y has minimum support value minsup if minsup percent of transactions support X ‰ Y , the rule X o Y holds with minimum confidence value minconf if minconf percent of transactions that support X also support Y. For example, from the transactions kept in supermarkets, an association rule such as “Bread and Butter o Milk” could be identified through association mining.

Generating frequent itemsets incrementally: Two novel approaches based on Galois lattice theory, Journal of Experimental & Theoretical Artificial Intelligence 14:2/3, 115–142. H. -C. H. W. Kwong (1998). Mining association rules with weighted items, Proceedings of 1998 International Database Engineering and Applications Symposium, Cardiff, Wales, 68–77. -F. Lu, H. Hu, F. Li (2001). Mining weighted association rules, Intelligent Data Analysis, 5, 211–225. 12 A. Mild, T. Reutterer (2003). An improved collaborative filtering approach for predicting cross-category purchases based on binary market basket data, Journal of Retailing & Consumer Services 10:3, 123–133.

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