MINING ASSOCIATION RULES FOR HIGH UTILITY ITEMSETS USING UP GROWTH+ ALGORITHM FROM TRANSACTIONAL DATABASES

Utility mining emerges as an important topic in data mining field. High utility item sets mining refers to importance or profitability of an item to users. Efficient mining of high utility itemsets plays an important role in many real-time applications and is an important research issue in data mining area. Number of Algorithms like apriori, FP Growth has been proposed in this area, but they cause the problem of generating a large number of candidate itemsets. That will lead to high requirement of space and time and hence the performance of mining will be less .It is not at all good when the database contains transactions having long size or high utility itemsets which also having long size. UP Growth+ algorithm is proposed in this paper for mining high utility itemsets. A UP Tree data structure is used for storing the information about high utility itemset such that by using only double scanning of database, candidate itemsets can be efficiently generate. These proposed algorithms will cause the reduction of the number of candidates effectively and also reduces the requirement for space and time, when a database contains large number of transactions.

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