Abstract
Pattern mining is an unsupervised data mining approach aims to find interesting patterns that can be used to support decision-making. High Utility Pattern Mining (HUPM) aims to extract patterns having high utility or importance which has broad applications in domains such as market basket analysis, product recommendation, bioinformatics, e-learning, text mining, and web click stream analysis. However, it has several limitations on real life scenarios; as a consequence, many extensions of HUPM appeared in the literature such as Correlated High Pattern Mining, Incremental Utility Mining, On-Shelf High Utility Pattern Mining, and Concise Representations of High Utility Patterns. The Correlated High Utility Pattern Mining aims to extract interesting high utility patterns by utilizing both Utility and Correlation measures. Several algorithms have been proposed to mine the correlated high utility patterns. These algorithms differ in the measures used to evaluate the interestingness of the patterns, data structures and pruning properties which they use to improve the mining performance. This paper presents a detailed survey on correlated high utility pattern mining, their methods, measures, data structures and pruning properties.
Highlights
We are living in the data age where a huge amount of data is generated by different devices on the daily basis
Each extension addresses a specific problem and has its own methods, measurements, data structures and pruning properties. In this survey we have focused on the Correlated High Utility Pattern Mining
ALGORITHMS FOR CORRELATED HIGH UTILITY PATTERN MINING In order to extract more interesting pattern and to avoid misleading patterns resulted from the traditional methods of High Utility Pattern Mining (HUPM), a number of methods have been proposed to mine correlated high utility patterns by utilizing both utility and correlation measures
Summary
We are living in the data age where a huge amount of data is generated by different devices on the daily basis. To solve the above stated issue, a number of algorithms have been proposed for mining patterns that are more interesting by utilizing both utility and correlation measures to find correlated high utility itemsets [16]- [21] These techniques differ from each other in the measures used to evaluate the interestingness of the extracted patterns, the data structures and pruning properties that they used to reduce the search space and improve the mining performance. Many HUPs may be not interesting due to the weak correlations among the items inside patterns [15] To address this limitation, researchers designed methods to extract correlated high utility patterns [16]- [21]. Each extension addresses a specific problem and has its own methods, measurements, data structures and pruning properties In this survey we have focused on the Correlated High Utility Pattern Mining. These measures with their methods are discussed
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