The investigation of discovering potential musical instruments teachers by effective data clustering scheme

Authors:
Cheng-Fa Tsai;Yu-Tai Su;Chiu-Yen Tsai;Chun-Yi Sung
Affiliations:
Management Information Systems Department, National Pingtung University of Science and Technology, Pingtung, Taiwan R.O.C.;Department of Western Music, The Chinese Culture University, Taipei, Taiwan R.O.C.;Management Information Systems Department, National Pingtung University of Science and Technology, Pingtung, Taiwan R.O.C.;Management Information Systems Department, National Pingtung University of Science and Technology, Pingtung, Taiwan R.O.C.
Venue:
WSEAS Transactions on Computers
Year:
2009

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Abstract

Data clustering plays an important role in various fields. Data clustering approaches have been designed in recent years. This investigation aims to present data clustering algorithm to identify potential musical instruments teachers. With a total of 5125 candidates registered respectively in 9 grades of Taiwan United Music Grade Test during 2000-2008. Moreover, this study proposes a new data clustering algorithm called MIDBSCAN and an existing well-known neural network called self-organizing map (SOM) to perform data clustering applications for discovering potential musical instruments teachers. The processing procedure of searching for neighbors (neighborhood data points) is very time consuming in the existing well-known DBSCAN and IDSCAN algorithms. Therefore, to shorten the time consumed, the proposed MIDBSCAN algorithm focuses lowering the number of expansion seeds added into the neighborhood data in this procedure, thus reducing the time cost of searching for neighbors. 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. MIDBSCAN outperforms SOM in execution time cost. It is feasible to perform data clustering analysis in various data mining applications using the proposed MIDBSCAN algorithm.