scholarly journals Efficient Chain Structure for High-Utility Sequential Pattern Mining

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 40714-40722 ◽  
Author(s):  
Jerry Chun-Wei Lin ◽  
Yuanfa Li ◽  
Philippe Fournier-Viger ◽  
Youcef Djenouri ◽  
Ji Zhang
Keyword(s):  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Chain Structure ◽  
Sequential Pattern ◽  
High Utility

Scalable Mining of High-Utility Sequential Patterns With Three-Tier MapReduce Model

2022 ◽  
Vol 16 (3) ◽  
pp. 1-26
Author(s):  
Jerry Chun-Wei Lin ◽  
Youcef Djenouri ◽  
Gautam Srivastava ◽  
Yuanfa Li ◽  
Philip S. Yu
Keyword(s):  
Large Scale ◽  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Main Memory ◽  
Frequent Itemset ◽  
Sequential Pattern ◽  
Sequential Patterns ◽  
Speed Up ◽  
Mapreduce Model ◽  
High Utility

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.

Mining Time-Interval Sequential Patterns with High Utility from Transaction Databases

2016 ◽  
Vol 20 (6) ◽  
pp. 1018-1026 ◽  
Author(s):  
Wen-Yen Wang ◽  
◽  
Anna Y.-Q. Huang ◽  
Keyword(s):  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Business Practice ◽  
Sequential Pattern ◽  
Sequential Patterns ◽  
Time Interval ◽  
Business Managers ◽  
Time Intervals ◽  
High Utility ◽  
Product Sales

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.

scholarly journals HIGH UTILITY ITEM INTERVAL SEQUENTIAL PATTERN MINING ALGORITHM

2020 ◽  
Vol 36 (1) ◽  
pp. 1-15
Author(s):  
Tran Huy Duong ◽  
Nguyen Truong Thang ◽  
Vu Duc Thi ◽  
Tran The Anh
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Sequential Patterns ◽  
Sequence Database ◽  
Mining Algorithm ◽  
Pattern Growth ◽  
High Utility ◽  
Growth Approach

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.

ProUM: High Utility Sequential Pattern Mining

2019 ◽  
Author(s):  
Wensheng Gan ◽  
Jerry Chun-Wei Lin ◽  
Jiexiong Zhang ◽  
Han-Chieh Chao ◽  
Hamido Fujita ◽  
...  
Keyword(s):  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
High Utility

Applying the maximum utility measure in high utility sequential pattern mining

2014 ◽  
Vol 41 (11) ◽  
pp. 5071-5081 ◽  
Author(s):  
Guo-Cheng Lan ◽  
Tzung-Pei Hong ◽  
Vincent S. Tseng ◽  
Shyue-Liang Wang
Keyword(s):  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Utility Measure ◽  
Maximum Utility ◽  
High Utility
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