Effective and Efficient Utility Mining Technique for
Incremental Dataset
Kavitha
JeyaKumar1, Manjula Dhanabalachandran1, and
Kasthuri JeyaKumar2
1Department of Computer
Science and Engineering, Anna University, India
2Department of
Electronics and Communication Engineering, Sri Ramaswami Memorial University,
India
Abstract: Traditional association rule mining, which
is based on frequency values of items, cannot meet the demands of different
factors in real world applications. Thus utility mining is presented to
consider additional measures, such as profit or price according to user
preference. Although several algorithms were proposed for mining high utility
itemsets, they incur the problem of producing large number of candidate
itemsets, results in performance degradation in terms of execution time and
space requirement. On the other hand when the data come intermittently, the
incremental and interactive data mining approach needs to be processed to
reduce unnecessary calculations by using previous data structures and mining
results. In this paper, an incremental mining algorithm for efficiently mining
high utility itemsets is proposed to handle the above situation. It is based on
the concept of Utility Pattern Growth (UP-Growth) for mining high utility
itemsets with a set of effective strategies for pruning candidate itemsets and
Fast Update (FUP) approach, which first partitions itemsets into four parts
according to whether they are high-transaction weighted utilization items in
the original and newly inserted transactions. Experimental results show that
the proposed Fast Update Utility Pattern Tree (FUUP) approach can thus achieve
a good trade between execution time and tree complexity.
Keywords: Data mining, utility mining, incremental mining.
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