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A comparative study of top-k high utility itemset mining methods

S. Krishnamoorthy

Published 2018 in Studies in Big Data

ABSTRACT

High Utility Itemset (HUI) mining problem is one of the important problems in the data mining literature. The problem offers greater flexibility to a decision maker to incorporate her/his notion of utility into the pattern mining process. The problem, however, requires the decision maker to choose a minimum utility threshold value for discovering interesting patterns. This is quite challenging due to the disparate itemset characteristics and their utility distributions. In order to address this issue, Top-K High Utility Itemset (THUI) mining problem was introduced in the literature. THUI mining problem is primarily a variant of the HUI mining problem that allows a decision maker to specify the desired number of HUIs rather than the minimum utility threshold value. Several algorithms have been introduced in the literature to efficiently mine top-k HUIs. This paper systematically analyses the top-k HUI mining methods in the literature, describes the methods, and performs a comparative analysis. The data structures, threshold raising strategies, and pruning strategies adopted for efficient top-k HUI mining are also presented and analysed. Furthermore, the paper reviews several extensions of the top-k HUI mining problem such as data stream mining, sequential pattern mining and on-shelf utility mining. The paper is likely to be useful for researchers to examine the key methods in top-k HUI mining, evaluate the gaps in literature, explore new research opportunities and enhance the state-of-the-art in high utility pattern mining.

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