Performance Analysis and Evaluation of Clustering Algorithms
Sanskruti Patel1, Atul Patel2
1Sanskruti Patel, Department of Computer Science and Applications, Charotar University of Science and Technology, Changa, Gujarat, India.
2Atul Patel, Department of Computer Science and Applications, Charotar University of Science and Technology, Changa, Gujarat, India.
Manuscript received on 10 April 2019 | Revised Manuscript received on 17 April 2019 | Manuscript Published on 24 May 2019 | PP: 179-184 | Volume-8 Issue-6S3 April 2019 | Retrieval Number: F22300486S219/19©BEIESP
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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: The Today’s digital world, data generation is growing at a rapid rate, almost doubling every two years. The extensive growth in the digital devices generates and consumes enormous data. In the field of Artificial Intelligence, machine learning provides a paradigm to recognize hid-den patterns in the data to perform useful inference using those patterns that have been learned. Clustering or cluster analysis is one of the most essential and important unsupervised learning technique. Clustering is a technique of natural grouping of data objects which are unlabeled and it forms these grouping in such a way that data objects belonging to one cluster are not similar to the objects belonging to another cluster. In this paper, different clustering approaches and techniques used in unsupervised learning are discussed. Also, four major clustering algorithms namely k-means, EM, hierarchical and make density based are applied on different datasets and their performance is analyzed by using certain parameters.
Keywords: Unsupervised Learning, Clustering Algorithms, K-means Clustering, Hierarchical Clustering, Density-Based Clustering.
Scope of the Article: Computer Science and Its Applications