Distributed Mining of High Utility Time Interval Sequential Patterns with Multiple Minimum Utility Thresholds

2021 ◽  
pp. 86-97
Author(s):  
Sumalatha Saleti ◽  
Jaya Lakshmi Tangirala ◽  
Ragunathan Thirumalaisamy
Keyword(s):  
Sequential Patterns ◽  
Time Interval ◽  
High Utility

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 An Algorithm for Mining High Utility Sequential Patterns with Time Interval

2019 ◽  
Vol 19 (4) ◽  
pp. 3-16
Author(s):  
Tran Huy Duong ◽  
Demetrovics Janos ◽  
Vu Duc Thi ◽  
Nguyen Truong Thang ◽  
Tran The Anh
Keyword(s):  
Sequential Patterns ◽  
Time Constraints ◽  
Time Interval ◽  
Sequence Database ◽  
Utility Value ◽  
Time Intervals ◽  
Real World Applications ◽  
High Utility ◽  
Downward Closure ◽  
Mining Algorithms

Abstract Mining High Utility Sequential Patterns (HUSP) is an emerging topic in data mining which attracts many researchers. The HUSP mining algorithms can extract sequential patterns having high utility (importance) in a quantitative sequence database. In real world applications, the time intervals between elements are also very important. However, recent HUSP mining algorithms cannot extract sequential patterns with time intervals between elements. Thus, in this paper, we propose an algorithm for mining high utility sequential patterns with the time interval problem. We consider not only sequential patterns’ utilities, but also their time intervals. The sequence weight utility value is used to ensure the important downward closure property. Besides that, we use four time constraints for dealing with time interval in the sequence to extract more meaningful patterns. Experimental results show that our proposed method is efficient and effective in mining high utility sequential pattern with time intervals.

Knowledge gathering of fuzzy multi-time-interval sequential patterns

2010 ◽  
Vol 180 (17) ◽  
pp. 3316-3334 ◽  
Author(s):  
Tony Cheng-Kui Huang
Keyword(s):  
Sequential Patterns ◽  
Time Interval

An efficient algorithm for Hiding High Utility Sequential Patterns

2018 ◽  
Vol 95 ◽  
pp. 77-92 ◽  
Author(s):  
Bac Le ◽  
Duy-Tai Dinh ◽  
Van-Nam Huynh ◽  
Quang-Minh Nguyen ◽  
Philippe Fournier-Viger
Keyword(s):  
Efficient Algorithm ◽  
Sequential Patterns ◽  
High Utility

Multi-core parallel algorithms for hiding high-utility sequential patterns

2021 ◽  
pp. 107793
Author(s):  
Ut Huynh ◽  
Bac Le ◽  
Duy-Tai Dinh ◽  
Hamido Fujita
Keyword(s):  
Parallel Algorithms ◽  
Sequential Patterns ◽  
High Utility

scholarly journals Mining significant high utility gene regulation sequential patterns

2017 ◽  
Vol 11 (S6) ◽  
Author(s):  
Morteza Zihayat ◽  
Heidar Davoudi ◽  
Aijun An
Keyword(s):  
Gene Regulation ◽  
Sequential Patterns ◽  
High Utility
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