Comparative evaluation of pattern mining techniques: an empirical study
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Comparative evaluation of pattern mining techniques: an empirical study Anindita Borah1 · Bhabesh Nath1 Received: 9 December 2019 / Accepted: 27 October 2020 © The Author(s) 2020
Abstract Pattern mining has emerged as a compelling field of data mining over the years. Literature has bestowed ample endeavors in this field of research ranging from frequent pattern mining to rare pattern mining. A precise and impartial analysis of the existing pattern mining techniques has therefore become essential to widen the scope of data analysis using the notion of pattern mining. This paper is therefore an attempt to provide a comparative scrutiny of the fundamental algorithms in the field of pattern mining through performance analysis based on several decisive parameters. The paper provides a structural classification of the widely referenced techniques in four pattern mining categories: frequent, maximal frequent, closed frequent and rare. It provides an analytical comparison of these techniques based on computational time and memory consumption using benchmark real and synthetic data sets. The results illustrate that tree based approaches perform exceptionally well over level wise approaches in case of dense data sets for all the categories. However, for sparse data sets, level wise approaches performed better than the former ones. This study has been carried out with an aim to analyze the pros and cons of the well known pattern mining techniques under different categories. Through this empirical study, an endeavor has been made to enable the researchers identify some fruitful and promising research directions in one of the most remarkable area of research, pattern mining. Keywords Association rule mining · Frequent itemsets · Pattern mining · Performance analysis
Introduction Enormous quantity of data generated by organizations, emphasize on the discovery of valuable and significant information that led to the emergence of the field of data mining. Data mining has established itself as an inspiring area of database research whose prime concern is to extract hidden and meaningful information from databases. An imperative area of data mining research is pattern mining that aims to identify momentous patterns and correlations existing within a database. Since its inception, a considerable amount of research has been carried out in the field of pattern mining targeting different kinds of patterns as well as the issues and challenges faced during their extraction [10,11,15]. After establishing itself as a compelling and fruitful research area
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Anindita Borah [email protected] Bhabesh Nath [email protected]
1
Department of Computer Science and Engineering, Tezpur University, Napaam, Tezpur, Sonitpur, Assam 784028, India
for over a decade, pattern mining demands for an overview and re-examination of the various techniques developed and let the researchers identify their pros and cons, in order to establish it as a cornerstone approach in the field of data mining. Pattern mining is the
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