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Title: | Distance based k-means clustering algorithm for determining number of clusters for high dimensional data |
Authors: | Mohamed Cassim Alibuhtto Nor Idayu Mahat |
Keywords: | Clustering High Dimensional Data K-means algorithm Optimal Cluster Simulation |
Issue Date: | Jan-2020 |
Publisher: | Growing Science |
Citation: | Alibuhtto, M.C., & Mahat, N.I.(2020).Distance based k-means clustering algorithm for determining number of clusters for high dimensional data. Decision science letters. 9(1), 51-58. |
Abstract: | Clustering is one of the most common unsupervised data mining classification techniques for splitting objects into a set of meaningful groups. However, the traditional k-means algorithm is not applicable to retrieve useful information / clusters, particularly when there is an overwhelming growth of multidimensional data. Therefore, it is necessary to introduce a new strategy to determine the optimal number of clusters. To improve the clustering task on high dimensional data sets, the distance based k-means algorithm is proposed. The proposed algorithm is tested using eighteen sets of normal and non-normal multivariate simulation data under various combinations. Evidence gathered from the simulation reveal that the proposed algorithm is capable of identifying the exact number of clusters. |
URI: | http://ir.lib.seu.ac.lk/handle/123456789/3905 |
Appears in Collections: | Research Articles |
Files in This Item:
File | Description | Size | Format | |
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Distance based k-means.pdf | 913.37 kB | Adobe PDF | View/Open |
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