Abstract
High-utility itemset mining (HUIM) in transaction databases has been extensively studied to discover interesting itemsets from users' purchase behaviors. With this, business managers can adjust their sale strategies appropriately to increase profit. HUIM approaches usually focus on the utility values of itemsets, but rarely evaluate the correlation of items in itemsets. Many high-utility itemsets are weakly correlated and have no real meaning. To address this issue, we suggest an algorithm, called CoHUI-Miner, to efficiently find correlated high-utility itemsets. In the proposed algorithm, we use the database projection mechanism to reduce the database size and present a new concept, called the prefix utility of projected transactions, to eliminate itemsets which do not satisfy the minimum threshold in the mining process. Experimental evaluation on two types of datasets from sparse to dense ones shows that CoHUI-Miner can efficiently mine correlated high-utility itemsets with regard to both execution time and memory usage.
Highlights
Data in a variety of applications are more and more expanded and exploited, especially for business activities, which attracts great attention of scientists
The experimental results on dense, moderate and very dense datasets prove that the improved algorithm we proposed shows better performance than the state-ofthe-art correlated high-utility itemset mining (CoHUIM) algorithm in both runtime and memory usage
The major difference between the two algorithms is that CoHUIM generates the candidate set in phase 1 and rescans the dataset several times in phase 2 to discover correlated high-utility itemsets (CoHUIs), whereas CoHUI-Miner uses the prefix utility of the projected transactions to define the utility of an itemset without generating candidates
Summary
Data in a variety of applications are more and more expanded and exploited, especially for business activities, which attracts great attention of scientists. Data mining is an interesting direction to study. It includes frequent itemsets [1]–[5], association rules [6]–[8], high utility itemsets [9]–[11], etc. Frequent pattern mining (FPM) is an interesting problem in the field of data mining, with the target of finding frequent itemsets from a transaction database. Frequent itemsets have been widely studied and play an important role in the context of association rule mining (ARM) [1],[2], [5] for analyzing customers’ buying behaviors. High-utility itemset mining (HUIM) was presented to mine itemsets having high-utility values (or profits) in a transaction database, and
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