Behaviour of Players on IPL Based on Fuzzy C Means
Sandeep S. Jain1, Riya Gupta2, Chetika Tiwari3, Navnoor Kaur4
1Sandeep Singh Jain, Department of Computer Science & Engineering, Chitkara University, Rajpura, India.
2Riya Gupta, Department of Computer Science & Engineering, Chitkara University, Rajpura, India.
3Chetika Tiwari, Department of Computer Science & Engineering, Chitkara University, Rajpura, India.
4Navnoor Kaur, Department of Computer Science & Engineering, Chitkara University, Rajpura, India.
Manuscript received on 08 August 2019 | Revised Manuscript received on 17 August 2019 | Manuscript Published on 26 August 2019 | PP: 150-154 | Volume-8 Issue-9S August 2019 | Retrieval Number: I10240789S19/19©BEIESP | DOI: 10.35940/ijitee.I1024.0789S19
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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 are being widely used in the field of data mining in order to accumulate similar data in the form of clusters. Indian Premiere League(IPL) is one of the most famous cricket leagues around the globe. In this paper, the dataset of IPL is used to cluster the players on the basis of various attributes. The authors ought to analyze both batsmen and bowlers in various clusters with the help of Fuzzy-c-means. The algorithm has been implemented to group the players in different clusters based on their performance in the IPL season of 2018. The pros and cons of the algorithm are also discussed and finally the experimental results are shown to highlight two main clusters i.e. above average and below average. The present work simulates the algorithm to distinguish only overseas player. In future this work can be extended for every player and form a recommendation model to identify best player or form best team.
Keywords: Clustering Algorithm, Fuzzy-C-Means, Clusters, IPL Dataset.
Scope of the Article: Fuzzy Logics