Discovering potential musical instruments teachers using data clustering approach

Authors:
Cheng-Fa Tsai;Yu-Tai Su;Chiu-Yen Tsai;Chun-Yi Sung
Affiliations:
Department of Management Information Systems, National Pingtung University of Science and Technology, Pingtung, Taiwan;Department of Western Music, Chinese Culture University, Taipei, Taiwan;Department of Management Information Systems, National Pingtung University of Science and Technology, Pingtung, Taiwan;Department of Management Information Systems, National Pingtung University of Science and Technology, Pingtung, Taiwan
Venue:
NN'09 Proceedings of the 10th WSEAS international conference on Neural networks
Year:
2009

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Abstract

This paper aims to introduce data clustering approach to discover potential musical instruments teachers. With a total of 5125 candidates registered respectively in 9 grades during 2000-2008. Moreover, this work presents a new data clustering algorithm named MIDBSCAN and an existing well-known neural network called self-organizing map (SOM) to perform data clustering applications for finding potential musical instruments teachers. According to our simulation results, the proposed MIDBSCAN approach has low execution time cost, a maximum deviation in clustering correctness rate and a maximum deviation in noise data filtering rate.