Time Variant Multi Perspective Hierarchical Clustering Algorithm for Predicting Student Interest in Sports Mining
A.Basheer Ahamed1, M. Mohamed Surputheen2
1A.Basheer Ahamed, Research Scholar, Department of Computer Science, Jamal Mohamed College (Autonomous), (Affiliated to Bharathidasan University), Tiruchirappalli, Tamilnadu, India.
2Dr. M. Mohamed Surputheen, Associate Professor, Department of Computer Science, Jamal Mohamed College (Autonomous), (Affiliated to Bharathidasan University) , Tiruchirappalli, Tamilnadu, India.

Manuscript received on November 20, 2019. | Revised Manuscript received on November 28, 2019. | Manuscript published on 30 November, 2019. | PP: 7313-7317 | Volume-8 Issue-4, November 2019. | Retrieval Number: D5291118419/2019©BEIESP | DOI: 10.35940/ijrte.D5291.118419

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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: Predicting performance of students in sports is analyzed and studied. There are many techniques identified for the prediction of sports interest and they are not producing expected value. Towards performance development, a novel time variant multi perspective hierarchical clustering approach towards user interest prediction. The proposed time variant model reads the sports log and groups them according to the time domain. The entire log has been split into different of clusters as like time window. Then using window log, the method splits the logs according to different sports. For each time window, the method identifies the list of actions or sports played or tagged or chat with other users. Using the class of log, the method identifies the category of sports log and for each category of sports, the method compute the sports strike strength (SSS). Based on the value of SSS, the method identifies the user interest. Similarly, the interest of the student at each time window has been identified and used to generate the knowledge. The proposed method improves the performance of sports interest prediction on students with less false ratio.
Keywords: Sports Analysis, Data Mining, ML, Sport Prediction, Hierarchical Clustering, TMHC, Sports Mining.
Scope of the Article: Data Mining.