SPM: Study of Different Techniques of Sequential Pattern Mining

Authors

  • Sujit R. Wakchaure Department of Computer Science & Engineering, Dr. A. P. J. Abdul Kalam University, Indore(M.P.) 452010
  • Rajeev G. Vishwakarma Department of Computer Science & Engineering, Dr. A. P. J. Abdul Kalam University, Indore (M.P.) 452010

Keywords:

Sequential Pattern Mining, Apriori Technique, Frequent pattern growth technique

Abstract

An essential part of data mining is the process of finding unexpected and significant patterns hidden within databases. In the past several years, one trend that has emerged in the field of data mining is the development of algorithms for the purpose of locating patterns in sequential data. Sequential Pattern Mining (SPM) is one of the most well-known data mining activities that can be performed on sequences. Finding relevant subsequence’s within a set of sequences is the goal of this process. The interestingness of a subsequence can be evaluated based on a number of factors, such as how frequently it occurs, how long it lasts, and how much profit it brings in. Because data is encoded as sequences in many domains, including genomics, e-learning, market basket analysis, information extraction, and webpage click-stream assessment, sequential pattern mining has numerous real - world applications. This is because sequences are used to organise the data in these fields. This paper provides a comprehensive review of recent research on sequential pattern mining and its various applications. The purpose of this article is to evaluate recent developments in sequential pattern mining as well as provide an overview to the field of sequential pattern mining. This article offers a structured study on SPM as well as an analysis of the approaches that are used by SPM. In addition to this, it discusses the concerns, research issues, and future developments that are associated with sequential pattern mining.

Downloads

Download data is not yet available.

References

Muhammad J. Alibasa, Rafael A. Calvo and Kalina Yacef. “Sequential Pattern Mining Suggests Wellbeing Supportive Behaviors”, 2019, IEEE.

Ji-Soo Kang, Ji-Won Baek and Kyungyong Chung. “PrefixSpan Based Pattern Mining Using Time Sliding Weight From Streaming Data”, 2020, IEEE.

Jerry Chun-Wei Lin, Gautam Srivastava, Yuanfa Li, Tzung-Pei Hong and Shyue-Liang Wang. “Mining High-Utility Sequential Patterns in Uncertain Databases”, 2020, International Conference on Big Data (Big Data), IEEE.

Saıd Jabbour, Jerry Lonlac and Lakhdar Saıs. “Mining Gradual Itemsets Using Sequential Pattern Mining”, 2019, IEEE.

Chunkai Zhang and Yiwen Zu. “An efficient parallel High Utility Sequential Pattern Mining algorithm”, 2019, 21st International Conference on High Performance Computing and Communications; 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems, IEEE.

Md.Mahamud Hasan and Sadia Zaman Mishu. “An Adaptive Method for Mining Frequent Itemsets Based on Apriori And FP Growth Algorithm”, 2017, IEEE.

Swati Nagori and Dr. Hemant Kumar Soni. “Issues and Research Challenges in Sequential Pattern Mining”, 2020, International Conference on Advances and Developments in Electrical and Electronics Engineering (ICADEE), IEEE.

Ingle Mayur Rajendra, Shri Chaitanya Vyas, Sanika Sameer Moghe, Deepali Deshmukh, Sachin Sakhare and Prof Sudhanshu Gonge. “Implementing a Hybrid of Efficient Algorithms for Mining Top-K High Utility Itemsets”, 2018, IEEE.

Bhargav C. Kachhadiya and Prof. Bhavesh Patel. “A Survey on Sequential Pattern Mining Algorithm for Web Log Pattern Data”, 2018, 2nd International Conference on Trends in Electronics and Informatics (ICOEI), IEEE.

WU Jia, LV Bing and CUI Wei. “An Improved Sequential Pattern mining Algorithm based on Large Dataset”, 2019, International Conference on Power Data Science (ICPDS), IEEE.

Shah Mohammed Nuruddin, Md. Didarul Islam, Md. Shafiqul Alam, Jesan Ahammed Ovi and Md. Ashraful Islam. “An Efficient Approach for Sequential Pattern Mining on GPU Using CUDA Platform”, 2020, IEEE.

Wensheng Gan, Jerry Chun-Wei Lin, Jiexiong Zhang, Han-Chieh Chao, Hamido Fujita and Philip S. Yu. “ProUM: High Utility Sequential Pattern Mining”, 2019, International Conference on Systems, Man and Cybernetics (SMC), IEEE.

Yu-Hao Ke, Jen-Wei Huang, Wei-Chen Lin and Bijay Prasad Jaysawal. “Finding Possible Promoter Binding Sites in DNA Sequences by Sequential Patterns Mining with Specific Numbers of Gaps”, 2020, IEEE.

Eirini Stamoulakatou, Andrea Gulino and Pietro Pinoli. “DLA: a Distributed, Location-based and Apriori-based Algorithm for Biological Sequence Pattern Mining”, 2018, IEEE.

Sudhakar Singh, Rakhi Garg and P. K. Mishra. “Observations on Factors Affecting Performance of MapReduce based Apriori on Hadoop Cluster”, 2016, International Conference on Computing, Communication and Automation (ICCCA), IEEE.

Mercy Nyasha Mlambo, Naison Gasela and Michael Bukohwo Esiefarienrhe. “Implementation and Analysis of Enhanced Apriori Using MapReduce”, 2018, IEEE.

S.Haseena, S.Manoruthra, P.Hemalatha and V.Akshaya. “Mining Frequent Item sets on Large Scale Temporal Data”, 2018, 2nd International conference on Electronics, Communication and Aerospace Technology (ICECA), IEEE.

Mohammad Javad Shayegan Fard and Parsa Asgari Namin. “Review of Apriori based Frequent Itemset Mining Solutions on Big Data”, 2020, 6th International Conference on Web Research (ICWR), IEEE.

Mohammad Javad Shayegan and Parsa Asgari Namin. “An Approach to Improve Apriori Algorithm for Extraction of Frequent Itemsets”, 2021, 7th International Conference on Web Research (ICWR), IEEE.

Pan Zhaopeng, Liu Peiyu and Yi Jing. “An Improved FP-tree Algorithm for Mining Maximal Frequent Patterns”, 2018, 10th International Conference on Measuring Technology and Mechatronics Automation, IEEE.

Lingxizhu, Yufeiguo and Jingyiwang. “Application of FPGrowth Algorithm of Sequential Pattern Mining on Container Maintenance Components Association”, 2020, 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), IEEE.

Classification of Prefix Growth based mining Technique

Downloads

Published

16.04.2023

How to Cite

Wakchaure, S. R. ., & Vishwakarma, R. G. . (2023). SPM: Study of Different Techniques of Sequential Pattern Mining. International Journal of Intelligent Systems and Applications in Engineering, 11(5s), 442–457. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2806

Issue

Vol. 11 No. 5s (2023): Special Issue on Applications of Advanced Engineering Technologies

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.