An effective and efficient grid-based data clustering algorithm using intuitive neighbor relationship for data mining

This paper presents a new data clustering technique. It is a new grid-based clustering scheme by intuitive neighbor relationship for enhancing data clustering performance. Compared to other algorithms, this improved grid-based clustering algorithm substantially decreases repetitive clustering checks...

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Bibliographic Details
Published in2015 International Conference on Machine Learning and Cybernetics (ICMLC) Vol. 2; pp. 478 - 483
Main Authors Cheng-Fa Tsai, Sheng-Chiang Huang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2015
Subjects
Algorithm design and analysis
Clustering algorithms
Clustering methods
Cybernetics
Data mining
Dataclustering
Density-based clustering
Machine learning algorithms
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DOI10.1109/ICMLC.2015.7340603

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Summary:This paper presents a new data clustering technique. It is a new grid-based clustering scheme by intuitive neighbor relationship for enhancing data clustering performance. Compared to other algorithms, this improved grid-based clustering algorithm substantially decreases repetitive clustering checks of neighboring grids and greatly improve the efficiency of data processing. Our simulations demonstrate that the proposed data clustering technique delivers better performance, in terms of clustering correctness rate and noise filtering rate, than perform other well-known existing algorithms, GOD-CS, CLIQUE and TING. To our best knowledge, the proposed data clustering technique may be the rapid method in the world currently.
DOI:10.1109/ICMLC.2015.7340603