scholarly journals Dramatically Reducing Search for High Utility Sequential Patterns by Maintaining Candidate Lists

Information ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 44
Author(s):  
Scott Buffett

A ubiquitous challenge throughout all areas of data mining, particularly in the mining of frequent patterns in large databases, is centered on the necessity to reduce the time and space required to perform the search. The extent of this reduction proportionally facilitates the ability to identify patterns of interest. High utility sequential pattern mining (HUSPM) seeks to identify frequent patterns that are (1) sequential in nature and (2) hold a significant magnitude of utility in a sequence database, by considering the aspect of item value or importance. While traditional sequential pattern mining relies on the downward closure property to significantly reduce the required search space, with HUSPM, this property does not hold. To address this drawback, an approach is proposed that establishes a tight upper bound on the utility of future candidate sequential patterns by maintaining a list of items that are deemed potential candidates for concatenation. Such candidates are provably the only items that are ever needed for any extension of a given sequential pattern or its descendants in the search tree. This list is then exploited to significantly further tighten the upper bound on the utilities of descendent patterns. An extension of this work is then proposed that significantly reduces the computational cost of updating database utilities each time a candidate item is removed from the list, resulting in a massive reduction in the number of candidate sequential patterns that need to be generated in the search. Sequential pattern mining methods implementing these new techniques for bound reduction and further candidate list reduction are demonstrated via the introduction of the CRUSP and CRUSPPivot algorithms, respectively. Validation of the techniques was conducted on six public datasets. Tests show that use of the CRUSP algorithm results in a significant reduction in the overall number of candidate sequential patterns that need to be considered, and subsequently a significant reduction in run time, when compared to the current state of the art in bounding techniques. When employing the CRUSPPivot algorithm, the further reduction in the size of the search space was found to be dramatic, with the reduction in run time found to be dramatic to moderate, depending on the dataset. Demonstrating the practical significance of the work, experiments showed that time required for one particularly complex dataset was reduced from many hours to less than one minute.

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Chunkai Zhang ◽  
Zilin Du ◽  
Yiwen Zu

High-utility sequential pattern mining (HUSPM) is an emerging topic in data mining, where utility is used to measure the importance or weight of a sequence. However, the underlying informative knowledge of hierarchical relation between different items is ignored in HUSPM, which makes HUSPM unable to extract more interesting patterns. In this paper, we incorporate the hierarchical relation of items into HUSPM and propose a two-phase algorithm MHUH, the first algorithm for high-utility hierarchical sequential pattern mining (HUHSPM). In the first phase named Extension, we use the existing algorithm FHUSpan which we proposed earlier to efficiently mine the general high-utility sequences (g-sequences); in the second phase named Replacement, we mine the special high-utility sequences with the hierarchical relation (s-sequences) as high-utility hierarchical sequential patterns from g-sequences. For further improvements of efficiency, MHUH takes several strategies such as Reduction, FGS, and PBS and a novel upper bounder TSWU, which will be able to greatly reduce the search space. Substantial experiments were conducted on both real and synthetic datasets to assess the performance of the two-phase algorithm MHUH in terms of runtime, number of patterns, and scalability. Conclusion can be drawn from the experiment that MHUH extracts more interesting patterns with underlying informative knowledge efficiently in HUHSPM.


2022 ◽  
Vol 16 (3) ◽  
pp. 1-26
Author(s):  
Jerry Chun-Wei Lin ◽  
Youcef Djenouri ◽  
Gautam Srivastava ◽  
Yuanfa Li ◽  
Philip S. Yu

High-utility sequential pattern mining (HUSPM) is a hot research topic in recent decades since it combines both sequential and utility properties to reveal more information and knowledge rather than the traditional frequent itemset mining or sequential pattern mining. Several works of HUSPM have been presented but most of them are based on main memory to speed up mining performance. However, this assumption is not realistic and not suitable in large-scale environments since in real industry, the size of the collected data is very huge and it is impossible to fit the data into the main memory of a single machine. In this article, we first develop a parallel and distributed three-stage MapReduce model for mining high-utility sequential patterns based on large-scale databases. Two properties are then developed to hold the correctness and completeness of the discovered patterns in the developed framework. In addition, two data structures called sidset and utility-linked list are utilized in the developed framework to accelerate the computation for mining the required patterns. From the results, we can observe that the designed model has good performance in large-scale datasets in terms of runtime, memory, efficiency of the number of distributed nodes, and scalability compared to the serial HUSP-Span approach.


Author(s):  
Wen-Yen Wang ◽  
◽  
Anna Y.-Q. Huang ◽  

The purpose of time-interval sequential pattern mining is to help superstore business managers promote product sales. Sequential pattern mining discovers the time interval patterns for items: for example, if most customers purchase product item <span class="bold">A</span>, and then buy items <span class="bold">B</span> and <span class="bold">C</span> after <span class="bold">r</span> to <span class="bold">s</span> and <span class="bold">t</span> to <span class="bold">u</span> days respectively, the time interval between <span class="bold">r</span> to <span class="bold">s</span> and <span class="bold">t</span> to <span class="bold">u</span> days can be provided to business managers to facilitate informed marketing decisions. We treat these time intervals as patterns to be mined, to predict the purchasing time intervals between <span class="bold">A</span> and <span class="bold">B</span>, as well as <span class="bold">B</span> and <span class="bold">C</span>. Nevertheless, little work considers the significance of product items while mining these time-interval sequential patterns. This work extends previous work and retains high-utility time interval patterns during pattern mining. This type of mining is meant to more closely reflect actual business practice. Experimental results show the differences between three mining approaches when jointly considering item utility and time intervals for purchased items. In addition to yielding more accurate patterns than the other two methods, the proposed UTMining_A method shortens execution times by delaying join processing and removing unnecessary records.


Author(s):  
UNIL YUN ◽  
KEUN HO RYU

Sequential pattern mining with constraints has been developed to improve the efficiency and effectiveness in mining process. Specifically, there are two interesting constraints for sequential pattern mining. First, some sequences are more important and others are less important. Weight constraints consider the importance of sequences and items within sequences. Second, patterns including only a few items are interesting if they have high support. Meanwhile, long patterns can be interesting although their supports are relatively small. Weight constraints and length-decreasing support constraints are two paradigms aimed at finding important sequential patterns and reducing uninteresting patterns. Although weight and length-decreasing support constraints are vital elements, it is hard to consider both constraints by using previous approaches. In this paper, we integrate weight and length-decreasing support constraints by pushing two constraints into the prefix projection growth method. For pruning techniques, we define the Weighted Smallest Valid Extension property and apply the property to our pruning methods for reducing search space. In performance test, we show that our algorithm mines important sequential patterns with length-decreasing support constraints.


2020 ◽  
Vol 36 (1) ◽  
pp. 1-15
Author(s):  
Tran Huy Duong ◽  
Nguyen Truong Thang ◽  
Vu Duc Thi ◽  
Tran The Anh

High utility sequential pattern mining is a popular topic in data mining with the main purpose is to extract sequential patterns with high utility in the sequence database. Many recent works have proposed methods to solve this problem. However, most of them does not consider item intervals of sequential patterns which can lead to the extraction of sequential patterns with too long item interval, thus making little sense. In this paper, we propose a High Utility Item Interval Sequential Pattern (HUISP) algorithm to solve this problem. Our algorithm uses pattern growth approach and some techniques to increase algorithm's performance.


2020 ◽  
Vol 36 (1) ◽  
pp. 1-15
Author(s):  
Tran Huy Duong ◽  
Nguyen Truong Thang ◽  
Vu Duc Thi ◽  
Tran The Anh

High utility sequential pattern mining is a popular topic in data mining with the main purpose is to extract sequential patterns with high utility in the sequence database. Many recent works have proposed methods to solve this problem. However, most of them does not consider item intervals of sequential patterns which can lead to the extraction of sequential patterns with too long item interval, thus making little sense. In this paper, we propose a High Utility Item Interval Sequential Pattern (HUISP) algorithm to solve this problem. Our algorithm uses pattern growth approach and some techniques to increase algorithm's performance.


Sequential pattern mining is a data mining approach; aims to discover common interesting patterns in sequence datasets, which attracted a significant research interest due to its real world applications in various fields such as web click stream mining, retail business, stock market and bio-informatics. Each sequence in sequence dataset is composed of time ordered events and each event is an item set. It discovers all frequent subsequences having frequency greater than the given minimum support threshold. Discovering sequential patterns is expensive with respect to mining time as well as the amount of memory used, because of aggressive search space growth due to generation of explosive number of frequent subsequences with the sequence length as well as count of distinct items and large volume of sequence dataset. So, research in this domain aims at developing effective data structures which address frequency counting and large search space as well as scalable algorithms to reduce the execution time and the amount of memory utilized. We propose two efficient data structures called Pre-order Post-order Coded Aggregate Tree (PPCA-Tree) for compact representation of the sequence dataset and Root-node List of First-Occurrence Sub Trees Map (RLFOST-Map) for efficient representation of projected databases. We also developed an efficient Partially ordered Sequential PAttern Mining algorithm called PSPAM and Parallel implementation of Partially ordered Sequential PAttern Mining algorithm called PAPSPAM based on PPCA-Tree using RLFOST-Map which eliminates reconstruction of the projected databases. Experimental analysis done on various synthetic datasets proves that our algorithms PSPAM and PAPSPAM outperform prefixspan and other conventional & state-of-the-art algorithms over dense datasets with better scalability.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 40714-40722 ◽  
Author(s):  
Jerry Chun-Wei Lin ◽  
Yuanfa Li ◽  
Philippe Fournier-Viger ◽  
Youcef Djenouri ◽  
Ji Zhang

2018 ◽  
Vol 10 (11) ◽  
pp. 4330 ◽  
Author(s):  
Xinglong Yuan ◽  
Wenbing Chang ◽  
Shenghan Zhou ◽  
Yang Cheng

Sequential pattern mining (SPM) is an effective and important method for analyzing time series. This paper proposed a SPM algorithm to mine fault sequential patterns in text data. Because the structure of text data is poor and there are many different forms of text expression for the same concept, the traditional SPM algorithm cannot be directly applied to text data. The proposed algorithm is designed to solve this problem. First, this study measured the similarity of fault text data and classified similar faults into one class. Next, this paper proposed a new text similarity measurement model based on the word embedding distance. Compared with the classic text similarity measurement method, this model can achieve good results in short text classification. Then, on the basis of fault classification, this paper proposed the SPM algorithm with an event window, which is a time soft constraint for obtaining a certain number of sequential patterns according to needs. Finally, this study used the fault text records of a certain aircraft as experimental data for mining fault sequential patterns. Experiment showed that this algorithm can effectively mine sequential patterns in text data. The proposed algorithm can be widely applied to text time series data in many fields such as industry, business, finance and so on.


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