ProUM: Projection-based utility mining on sequence data

Abstract

Utility is an important concept in economics. A variety of applications consider utility in real-life situations, which has lead to the emergence of utility-oriented mining (also called utility mining) in the recent decade. Utility mining has attracted a great amount of attention, but most of the existing studies have been developed to deal with itemset-based data. Time-ordered sequence data is more commonly seen in real-world situations, which is different from itemset-based data. Since they are time-consuming and require large amount of memory usage, current utility mining algorithms still have limitations when dealing with sequence data. In addition, the mining efficiency of utility mining on sequence data still needs to be improved, especially for long sequences or when there is a low minimum utility threshold. In this paper, we propose an efficient Projection-based Utility Mining (ProUM) approach to discover high-utility sequential patterns from sequence data. The utility-array structure is designed to store the necessary information of the sequence-order and utility. ProUM can significantly improve the mining efficiency by utilizing the projection technique in generating utility-array, and it effectively reduces the memory consumption. Furthermore, a new upper bound named sequence extension utility is proposed and several pruning strategies are further applied to improve the efficiency of ProUM. By taking utility theory into account, the derived high-utility sequential patterns have more insightful and interesting information than other kinds of patterns. Experimental results showed that the proposed ProUM algorithm significantly outperformed the state-of-the-art algorithms in terms of execution time, memory usage, and scalability.

Efficient Chain Structure for High-Utility Sequential Pattern Mining

“…However, it is still challenging to find the HUSPs from a very big dataset. In addition, Gan et al then presented a projection method called ProUM [7] and the HUSP-ULL [8] to efficiently mine the HUSPs, which are the state-of-the-art approaches based on the utility-list structure.…”

“…Based on the I-Concatenation and S-Concatenation for the enumeration tree, all the possible and promising candidates can be produced and explored. In order to ensure the integrity of the mining results, we should concatenate items in a certain order [7], [8]. It is noted that the definition of sequence order is also suitable for q-sequence.…”

“…For the HUS-Span [29] and ProUM [7], it needs to generate the projection database of a sequence t using the original database. A designed sequence-utility (SU)-Chain here can be considered to produce the projection database for the sequence.…”

“…However, the task of HUSPM is more complex than that of traditional SPM since the sequence utility does not hold the downward closure property, thus the search space to discover the required HUSPs has become huge. Many algorithms were presented to design the pruning strategies and new upperbound values to reduce the search space such as USpan [32], HUS-Span [29], ProUM [7] and HUSP-ULL [8]. USpan was introduced to utilize the lexicographic quantitative sequence tree for mining the HUSPs, but the upper-bound value is overestimated, thus the search space to find the required information is too huge.…”

“…Moreover, the used PEU in the HUS-Span is not updated at each iteration, thus the upper bound is still overestimated; more unpromising candidates are still required to be determined. ProUM [7] and HUSP-ULL [8] are the state-of-the-art approaches by introducing the projection mechanism and efficient pruning strategies to mine the HUSPs.…”