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Review on recent developments in frequent itemset based document clustering, its research trends and applications

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  • Dharmendra Singh Rajput

Abstract

The document data is growing at an exponential rate. It is heterogeneous, dynamic and highly unstructured in nature. These characteristics of document data pose new challenges and opportunities for the development of various models and approaches for documents clustering. Different methods adopted for the development of these models. But these techniques have their advantages and disadvantages. The primary focus of the study is to the analysis of existing methods and approaches for document clustering based on frequent itemsets. Subsequently, this research direction facilitates the exploration of the emerging trends for each extension with applications. In this paper, more than 90 recent (published after 1990) research papers are summarised that are published in various reputed journals like IEEE Transaction, ScienceDirect, Springer-link, ACM and few fundamental authoritative articles.

Suggested Citation

  • Dharmendra Singh Rajput, 2019. "Review on recent developments in frequent itemset based document clustering, its research trends and applications," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 11(2), pages 176-195.
    • Handle: RePEc:ids:injdan:v:11:y:2019:i:2:p:176-195
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    Cited by:

    1. Harshita Patel & Dharmendra Singh Rajput & G Thippa Reddy & Celestine Iwendi & Ali Kashif Bashir & Ohyun Jo, 2020. "A review on classification of imbalanced data for wireless sensor networks," International Journal of Distributed Sensor Networks, , vol. 16(4), pages 15501477209, April.
    2. Abha Sharma & Pushpendra Kumar & Kanojia Sindhuben Babulal & Ahmed J. Obaid & Harshita Patel, 2022. "Categorical Data Clustering Using Harmony Search Algorithm for Healthcare Datasets," International Journal of E-Health and Medical Communications (IJEHMC), IGI Global, vol. 13(4), pages 1-15, August.

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