Review of Traditional and Ensemble Clustering Algorithms for High Dimensional Data
Proceedings of 3rd International Conference on Internet of Things and Connected Technologies (ICIoTCT), 2018 held at Malaviya National Institute of Technology, Jaipur (India) on March 26-27, 2018
7 Pages Posted: 9 May 2018
Date Written: April 28, 2018
High-dimensional data is explained by a huge quantity of features, introduces new issues to clustering. The so-named 'high dimensionality', creates initially to explain the common increase in time complexity of several computational issues, so the performances of the general clustering algorithms are unsuccessful. Accordingly, several works have been focused on introducing new techniques and clustering algorithms for handling higher dimensionality data. Regular to all clustering algorithms is the fact with the purpose of they need a various fundamental evaluation of similarity among data objects. However still, the existing clustering algorithms have some open research issues. In this review work, we provide a summary of the result of high-dimensional data space and their implications for various clustering algorithms. It also presents a detailed overview of many clustering algorithms with several types: subspace methods, modelbased clustering, density-based clustering methods; partition based clustering methods, etc., including a more detailed description of recent work of their own advantages and disadvantages for solving higher dimensionality data problem. The scope of the future work to extend the present clustering methods and algorithms are also discussed at end of the work.
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