Performance Examination of Hard Clustering Algorithm with Distance Metrics
Simhachalam Boddana1, Hymavathi Talla2
1Simhachalam Boddana, Department of Mathematics, GITAM University, Visakhapatnam (Andhra Pradesh), India.
2Hymavathi Talla, Department of Appied Mathematics, Dr. MRAR PG Center, Krishna University, (Andhra Pradesh), India.
Manuscript received on 23 November 2019 | Revised Manuscript received on 11 December 2019 | Manuscript Published on 30 December 2019 | PP: 172-178 | Volume-9 Issue-2S3 December 2019 | Retrieval Number: B10451292S319/2019©BEIESP | DOI: 10.35940/ijitee.B1045.1292S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Clustering algorithms based on partitions are widely us ed in unsupervised data analysis. K-means algorithm is one the efficient partition based algorithms ascribable to its intelligibility in computational cost. Distance metric has a noteworthy role in the efficiency of any clustering algorithm. In this work, K-means algorithms with three distance metrics, Hausdorff, Chebyshev and cosine distance metrics are implemented from the UC Irvine ml-database on three well-known physical-world data-files, thyroid, wine and liver diagnosis. The classification performance is evaluated and compared based on the clustering output validation and using popular Adjusted Rand and Fowlkes-Mallows indices compared to the repository results. The experimental results reported that the algorithm with Hausdorff distance metric outperforms the algorithm with Chebyshev and cosine distance metrics.
Keywords: Chebyshev Distance, Cosine Distance, Distance Metrics, Hausdorff Distance, K-means.
Scope of the Article: Algorithm Engineering