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The main purpose of data mining and analytics is to find novel, potentially useful patterns that can be utilized in real-world applications to derive beneficial knowledge. For identifying and evaluating the usefulness of different kinds of patterns, many techniques and constraints have been proposed, such as support, confidence, sequence order, and utility parameters (e.g., weight, price, profit, quantity, satisfaction, etc.). In recent years, there has been an increasing demand for utility-oriented pattern mining (UPM, or called utility mining). UPM is a vital task, with numerous high-impact applications, including cross-marketing, e-commerce, finance, medical, and biomedical applications. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods of UPM. First, we introduce an in-depth understanding of UPM, including concepts, examples, and comparisons with related concepts. A taxonomy of the most common and state-of-the-art approaches for mining different kinds of high-utility patterns is presented in detail, including Apriori-based, tree-based, projection-based, vertical-/horizontal-data-format-based, and other hybrid approaches. A comprehensive review of advanced topics of existing high-utility pattern mining techniques is offered, with a discussion of their pros and cons. Finally, we present several well-known open-source software packages for UPM. We conclude our survey with a discussion on open and practical challenges in this field. [ABSTRACT FROM AUTHOR]

From web search and data mining, users’ click and purchase behaviors contain valuable information, thus numerous approaches have been proposed to identify embedded useful knowledge from them. In these real-life situations, each user may perform the same action/event multiple times, and multiple accessed events product different profit. Many utility-oriented data mining approaches thus have been extensively studied. Previous studies have the limitation that the overall utility of traditional pattern is limited since they rarely consider the inherent correlation. For example, from the purchase behavior, the low-utility patterns sometimes with a very high-utility pattern will be considered as a valuable pattern even if this behavior may be not highly correlated. A more intelligent framework that provides non-redundant and correlated behavior based on utility measure is thus desired. In this paper, we first present a novel method to extract non-redundant correlated purchase behaviors considering the utility and correlation factors. The high qualified patterns can be derived with high profit and strong correlation, which can lead to higher recall and reveal better precision. In the proposed projection-based approach, an efficient projection mechanism and a sorted downward closure property are developed to reduce the database size. Several pruning strategies are further developed to efficiently and effectively discover the desired patterns. An extensive experimental study showed that the novel non-redundant correlated high-utility pattern has more effectiveness than the previous knowledge representation. Moreover, the proposed algorithm is efficient in terms of execution time and memory usage. [ABSTRACT FROM AUTHOR]

In the field of data mining, the topic of high-utility itemset mining (HUIM) has recently gained a lot of attention from researchers as it takes many factors into account that are useful for decision-making by retail managers. In the past, many algorithms have been presented for HUIM but most of them suffer from the limitation of using a single minimum utility threshold to identify high-utility itemsets (HUIs). For real-life applications, finding itemsets using a single threshold is inadequate and unfair since each item is different. Hence, the diversity or importance of each item should be considered. This paper proposes a solution to this issue by defining the novel task of HUIM with multiple minimum utility thresholds (named as HUIM-MMU). This task lets users specify a different minimum utility threshold for each item to identify more useful and specific HUIs, which would generate more profits when compared to HUIs discovered based on a single minimum utility threshold. The HUI-MMU algorithm is designed to mine HUIs in a level-wise manner. The sorted downward closure ( SDC ) property and the least minimum utility ( LMU ) concept are developed to avoid a combinatorial explosion for identifying HUIs and to ensure the completeness and correctness of HUI-MMU for discovering HUIs. Meanwhile, two improved algorithms, namely HUI-MMU TID and HUI-MMU TE , are presented based on the TID-index and EUCP strategies. Those strategies can be used to speed up the mining performance to discover HUIs. Substantial experiments on both real-life and synthetic datasets show that the designed algorithms can efficiently and effectively discover the complete set of HUIs in databases by considering multiple minimum utility thresholds. [ABSTRACT FROM AUTHOR]

High utility itemset mining is an emerging data mining task, which consists of discovering highly profitable itemsets (called high utility itemsets) in very large transactional databases. Many algorithms have been proposed to efficiently discover high utility itemsets but most of them assume that items may only have positive unit profits. However, in real-world transactional databases, items (products) often have positive or negative unit profits. Mining high utility itemsets in a transactional database where items have positive or negative unit profits is a computationally expensive task, and it is thus desirable to design more efficient algorithms. To address this issue, we propose an efficient algorithm named FHN (Faster High-Utility itemset miner with Negative unit profits). It relies on a novel PNU-list structure (Positive-and-Negative Utility-list) structure to efficiently mine high utility itemsets, while considering both positive and negative unit profits. Moreover, several pruning strategies are introduced in FHN to reduce the number of candidate itemsets, and thus enhance the performance of FHN. Extensive experimental results on both real-life and synthetic datasets show that the proposed FHN algorithm is in general two to three orders of magnitude faster and can use up to 200 times less memory than the state-of-the-art algorithm HUINIV-Mine. Moreover, it is shown that FHN performs especially well on dense datasets. [ABSTRACT FROM AUTHOR]