AN IMPROVED AND EFFICIENT METHOD TO DISCOVER THE FREQUENT PATTERNS FROM TARGETED PATTERNS IN TRANSACTIONAL DATASET USING TPIITR-FPMM

Saravanan Suba, Christopher. T

Abstract


In Recent years, Data mining is an essential technique to discover useful knowledge from transactional dataset. Association analysis algorithm is one of the vital data mining techniques. It normally catches relationships among items in transactional dataset. Generally they are used to develop the strategy of the future business. The main step of association analysis is to catch the frequent patterns in large transactional dataset. Plenty of methods are available in the literature to catch the frequent patterns. Most of the techniques gave in the literature catch all frequent itemsets for a specified minimum support threshold value. But in some instance, it is desired to examine the existence of some of the few targeted patterns (for example special offer given for group of items to promote the retail sales) in large transactional dataset to develop the strategy of the future business. For this purpose, we previously introduced SIFPMM (Selective Itemsets Frequent Pattern Mining Method) method and TM-PIFPMM (Transaction Merging-Predefined Itemsets Frequent Pattern Mining Method). To improve the performance of TM-PIFPMM, this TPIITR-FPMM (Targeted Patterns Involved Items Transaction Reduction-Frequent Pattern Mining Method) is proposed and the performance of this method is compared with Apriori, FP-Growth, SIFPMM and TM-PIFPMM. The Experimental analysis of TPIITR-FPMM verifies that this method outperforms than Apriori, FP-Growth, SIFPMM and TM-PIFPMM.

Keywords


Apriori; FP-tree; SIFPMM; TM-PIFPMM; TPIITR-FPMM, Minimum Support

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DOI: https://doi.org/10.26483/ijarcs.v8i9.5187

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