Efficient Data Mining Methodology for Sports
Prabu M1, Sudhaghar J2, Viswajith R3, Venkata Narsimha I4, Srikaanth A K5
1Prabu M, Assistant Professor, Department of Computer Science & Engineering, Ramapuram Campus, SRM Institute of Science and Technology, Chennai (TamilNadu), India.
2Sudhaghar J, Undergraduate Student, Department of Computer Science & Engineering, Ramapuram Campus, SRM Institute of Science and Technology, Chennai (TamilNadu), India.
3Viswajith R, Undergraduate Student, Department of Computer Science & Engineering, Ramapuram Campus, SRM Institute of Science and Technology, Chennai (TamilNadu), India.
4Venkata Narsimha I, Undergraduate Student, Department of Computer Science & Engineering, Ramapuram Campus, SRM Institute of Science and Technology, Chennai (TamilNadu), India.
5Srikaanth A K, Undergraduate Student, Department of Computer Science & Engineering, Ramapuram Campus, SRM Institute of Science and Technology, Chennai (TamilNadu), India.
Manuscript received on 04 April 2019 | Revised Manuscript received on 11 April 2019 | Manuscript Published on 26 April 2019 | PP: 81-84 | Volume-8 Issue-6S April 2019 | Retrieval Number: F60270486S19/19©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: Data Mining is a technique which used in various kinds of fields in the industry and they are helpful for collecting information and make use of it in fields. In sports transfiguring data into actionable information coaches, trainers can use data mining methods to tactic drill sessions and to decreasing the effect of activity testing on athletes. This paper uses a model where it can use cluster of techniques like as K-means, Ex-pectation maximization and Hierarchical Clustering to examine physiological data tested during incremental test runs. Evaluating the progress of session that have tested ,we assign tested athlete into different groups and monitor the result thereby improving the performance of the athlete.
Keywords: K-Means, Hierarchial Cluster.
Scope of the Article: Computer Science and Its Applications