International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF

Volume 67 | Issue 4 | Year 2019 | Article Id. IJCTT-V67I4P107 | DOI : https://doi.org/10.14445/22312803/IJCTT-V67I4P107

The Application of Data Mining in Sports and Extracurricular Activities

Tansen Patel, Uttam Kumar Sahu

Citation :

Tansen Patel, Uttam Kumar Sahu, "The Application of Data Mining in Sports and Extracurricular Activities," International Journal of Computer Trends and Technology (IJCTT), vol. 67, no. 4, pp. 33-36, 2019. Crossref, https://doi.org/10.14445/22312803/IJCTT-V67I4P107

Abstract

Data mining has been playing a more and more important role in all fields and had an increasingly greater influence. By using association analysis of data mining and classification algorithm, this paper analyzes the result data of college sports and extracurricular activities, which were held in last four year’s data of csvtu - youth festival. It studies the correlation between different students, branches, semesters, events and years. This research paper use k-means technique in data mining for mining our dataset which comes under clustering techniques of data mining. WEKA software is used to implement for the result part. The paper suggests the implementation of k-means Clustering technique of data mining.Data mining has been playing a more and more important role in all fields and had an increasingly greater influence. By using association analysis of data mining and classification algorithm, this paper analyzes the result data of college sports and extracurricular activities, which were held in last four year’s data of csvtu - youth festival. It studies the correlation between different students, branches, semesters, events and years. This research paper use k-means technique in data mining for mining our dataset which comes under clustering techniques of data mining. WEKA software is used to implement for the result part. The paper suggests the implementation of k-means Clustering technique of data mining.

Keywords

Data Mining, WEKA, Clustering, k-means.

References

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