  High utility sequential pattern mining (HUSPM) aims to mine all patterns that
yield a high utility (profit) in a sequence dataset. HUSPM is useful for
several applications such as market basket analysis, marketing, and website
clickstream analysis. In these applications, users may also consider high
utility patterns frequently appearing in the dataset to obtain more fruitful
information. However, this task is high computation since algorithms may
generate a combinatorial explosive number of candidates that may be redundant
or of low importance. To reduce complexity and obtain a compact set of frequent
high utility sequential patterns (FHUSPs), this paper proposes an algorithm
named CHUSP for mining closed frequent high utility sequential patterns
(CHUSPs). Such patterns keep a concise representation while preserving the same
expressive power of the complete set of FHUSPs. The proposed algorithm relies
on a CHUS data structure to maintain information during mining. It uses three
pruning strategies to eliminate early low-utility and non-frequent patterns,
thereby reducing the search space. An extensive experimental evaluation was
performed on six real-life datasets to evaluate the performance of CHUSP in
terms of execution time, memory usage, and the number of generated patterns.
Experimental results show that CHUSP can efficiently discover the compact set
of CHUSPs under different user-defined thresholds.
