Clustering approaches for high‐dimensional databases: A review

Abstract

Data mining is an inevitable task in most of the emerging computing technologies as it debilitates the complexity of datasets by rendering a better insight. Moreover, it entails the efficacy to envisage ingeniously the vast and heterogeneous datasets and thus delineates substantial knowledge from the abundance of data by pragmatic implementation of suitable algorithm. There are galore of algorithms in literature for this purpose. Furthermore, clustering is widely used techniques to analyze the data within the purview of data mining and thus it became as a motivational impetus for the authors to survey the existing literature on this topic rigorously and have consequently identified various key parameters so that concomitant improvement can be possible while selecting a best fit clustering algorithm pertaining to a specific problem domain. Furthermore, clustering, classification and association rule mining are akin and indispensable to data mining and owing to these authors have also included interrelation and intertwining among these terms so that this work will presage chunk of help for the researchers working in this field. The present study also envisages and manifests the challenges associated with the clustering algorithms for two‐ and high‐dimensional databases in a flamboyant fashion. Over and above, this work identifies key parametric attributes to assess the clustering algorithms which in turn benevolent the existing work and paves the way for profound future research in this realm. This article is categorized under: Technologies > Structure Discovery and Clustering Technologies > Classification Technologies > Association Rules Fundamental Concepts of Data and Knowledge > Big Data Mining

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