A Modified K-means Algorithms - Bi-Level K-Means Algorithm

DOI

10.2991/scict-14.2014.3How to use a DOI?

Keywords

Clustering; K-means algorithm; Classification; Genetic algorithm

Abstract

In this paper, a modified K-means algorithm is proposed to categorize a set of data into smaller clusters. K-means algorithm is a simple and easy clustering method which can efficiently separate a huge number of continuous numerical data with high-dimensions. Moreover, the data in each cluster are similar to one another. However, it is vulnerable to outliers and noisy data, and it spends much executive time in partitioning data too. Noisy data, outliers, and the data with quite different values in one cluster may reduce the accuracy rate of data clustering since the cluster center cannot precisely describe the data in the cluster. In this paper, a bi-level K-means algorithm is hence provided to solve the problems mentioned above. The bi-level K-means algorithm can give an expressive experimental results.

Copyright

© 2014, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Shyr-Shen Yu
AU  - Shao-Wei Chu
AU  - Ching-Lin Wang
AU  - Yung-Kuan Chan
AU  - Chia -Yi Chuang
PY  - 2014/05
DA  - 2014/05
TI  - A Modified K-means Algorithms - Bi-Level K-Means Algorithm
BT  - Proceedings of the 2nd International Conference on Soft Computing in Information Communication Technology
PB  - Atlantis Press
SP  - 10
EP  - 13
SN  - 1951-6851
UR  - https://doi.org/10.2991/scict-14.2014.3
DO  - 10.2991/scict-14.2014.3
ID  - Yu2014/05
ER  -