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Discovery of Frequent Itemsets based on Minimum Quantity and Support
Preetham Kumar, Ananthanarayana V S
Pages - 216 - 225     |    Revised - 05-08-2009     |    Published - 01-09-2009
Volume - 3   Issue - 3    |    Publication Date - June 2009  Table of Contents
Channel allocation problem, tabu search, simulated annealing
Most of the association rules mining algorithms to discover frequent itemsets do not consider the components of transactions such as number of items bought or quantity and its total cost. In a large database it is possible that even if the itemset appears in a very few transactions, it may be purchased in a large quantity for every transaction in which it is present, and may lead to very high profit. Therefore the quantity and its total cost of the item bought are the most important components, without which it may lead to loss of information. Our novel method discovers all frequent itemsets based on items quantity, in addition to the discovery of frequent itemsets based on user defined minimum support. In order to achieve this, we first construct a tree containing the quantities of the items bought as well as the transactions which do not contain these items in a single scan of the database. Then by scanning the tree we can discover all frequent itemset based on user defined minimum quantity as well as support. This method is also found to be more efficient than Apriori and FP-tree, which require multiple scans of the database to discover all frequent itemsets based on user defined minimum support.
CITED BY (3)  
1 Jabbar, M. A., Deekshatulu, B. L., & Chandra, P. (2016). A Novel Algorithm for Utility-Frequent Itemset Mining in Market Basket Analysis. In Innovations in Bio-Inspired Computing and Applications (pp. 337-345). Springer International Publishing.
2 Kumar, G., Mangla, N., & Mahajan, A. (2011). Improved Data Mining Based on Semantic Similarity to Mine New Association Rules. Data Mining and Knowledge Engineering, 3(4), 223-230.
3 B. D. Satoto, D. O. Siahaan and A. Saikhu, “Perbaikan Struktur Weighted Tree Dengan Metode Partisi Fuzzy Dalam Pembangkitan Frequent Itemset”, Journal Ilmiah, KURSOR, Menuju Solusi Teknologi Informasi, 5(3), pp. 175-185, 2010.
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Mr. Preetham Kumar
- India
Mr. Ananthanarayana V S
- India