arXiv subjects

Jiexiong Zhang

Publications and source records attributed to Jiexiong Zhang.

4 recordsLinked to original sources

arXiv2019

Utility Mining Across Multi-Sequences with Individualized Thresholds

Utility-oriented pattern mining has become an emerging topic since it can reveal high-utility patterns (e.g., itemsets, rules, sequences) from different types of data, which provides more information than the traditional frequent/confident-based pattern mining models. The utilities of various items are not exactly equal in realistic situations; each item has its own utility or importance. In general, user considers a uniform minimum utility (minutil) threshold to identify the set of high-utility sequential patterns (HUSPs). This is unable to find the interesting patterns while the minutil is set extremely high or low. We first design a new utility mining framework namely USPT for mining high-Utility Sequential Patterns across multi-sequences with individualized Thresholds. Each item in the designed framework has its own specified minimum utility threshold. Based on the lexicographic-sequential tree and the utility-array structure, the USPT framework is presented to efficiently discover the HUSPs. With the upper-bounds on utility, several pruning strategies are developed to prune the unpromising candidates early in the search space. Several experiments are conducted on both real-life and synthetic datasets to show the performance of the designed USPT algorithm, and the results showed that USPT could achieve good effectiveness and efficiency for mining HUSPs with individualized minimum utility thresholds.

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arXiv2019

ProUM: Projection-based Utility Mining on Sequence Data

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.

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arXiv2019

Fast Utility Mining on Complex Sequences

High-utility sequential pattern mining is an emerging topic in the field of Knowledge Discovery in Databases. It consists of discovering subsequences having a high utility (importance) in sequences, referred to as high-utility sequential patterns (HUSPs). HUSPs can be applied to many real-life applications, such as market basket analysis, E-commerce recommendation, click-stream analysis and scenic route planning. For example, in economics and targeted marketing, understanding economic behavior of consumers is quite challenging, such as finding credible and reliable information on product profitability. Several algorithms have been proposed to address this problem by efficiently mining utility-based useful sequential patterns. Nevertheless, the performance of these algorithms can be unsatisfying in terms of runtime and memory usage due to the combinatorial explosion of the search space for low utility threshold and large databases. Hence, this paper proposes a more efficient algorithm for the task of high-utility sequential pattern mining, called HUSP-ULL. It utilizes a lexicographic sequence (LS)-tree and a utility-linked (UL)-list structure to fast discover HUSPs. Furthermore, two pruning strategies are introduced in HUSP-ULL to obtain tight upper-bounds on the utility of candidate sequences, and reduce the search space by pruning unpromising candidates early. Substantial experiments both on real-life and synthetic datasets show that the proposed algorithm can effectively and efficiently discover the complete set of HUSPs and outperforms the state-of-the-art algorithms.

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arXiv2019

Utility Mining Across Multi-Dimensional Sequences

Knowledge extraction from database is the fundamental task in database and data mining community, which has been applied to a wide range of real-world applications and situations. Different from the support-based mining models, the utility-oriented mining framework integrates the utility theory to provide more informative and useful patterns. Time-dependent sequence data is commonly seen in real life. Sequence data has been widely utilized in many applications, such as analyzing sequential user behavior on the Web, influence maximization, route planning, and targeted marketing. Unfortunately, all the existing algorithms lose sight of the fact that the processed data not only contain rich features (e.g., occur quantity, risk, profit, etc.), but also may be associated with multi-dimensional auxiliary information, e.g., transaction sequence can be associated with purchaser profile information. In this paper, we first formulate the problem of utility mining across multi-dimensional sequences, and propose a novel framework named MDUS to extract Multi-Dimensional Utility-oriented Sequential useful patterns. Two algorithms respectively named MDUS_EM and MDUS_SD are presented to address the formulated problem. The former algorithm is based on database transformation, and the later one performs pattern joins and a searching method to identify desired patterns across multi-dimensional sequences. Extensive experiments are carried on five real-life datasets and one synthetic dataset to show that the proposed algorithms can effectively and efficiently discover the useful knowledge from multi-dimensional sequential databases. Moreover, the MDUS framework can provide better insight, and it is more adaptable to real-life situations than the current existing models.

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