@inproceedings{8dfd40eb97064ffe9291fd27dba84d06,
title = "Neighborhood density method for selecting initial cluster centers in K-means clustering",
abstract = "This paper presents a new method for effectively selecting initial cluster centers in k-means clustering. This method identifies the high density neighborhoods from the data first and then selects the central points of the neighborhoods as initial centers. The recently published Neighborhood-Based Clustering (NBC) algorithm is used to search for high density neighborhoods. The new clustering algorithm NK-means integrates NBC into the k-means clustering process to improve the performance of the k-means algorithm while preserving the k-means efficiency. NBC is enhanced with a new cell-based neighborhood search method to accelerate the search for initial cluster centers. A merging method is employed to filter out insignificant initial centers to avoid too many clusters being generated. Experimental results on synthetic data sets have shown significant improvements in clustering accuracy in comparison with the random k-means and the refinement k-means algorithms.",
keywords = "Clustering, Initial Cluster Center Selection, K-means, Neighborhood-Based Clustering",
author = "Yunming Ye and Huang, \{Joshua Zhexue\} and Xiaojun Chen and Shuigeng Zhou and Graham Williams and Xiaofei Xu",
year = "2006",
doi = "10.1007/11731139\_23",
language = "英语",
isbn = "3540332065",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "189--198",
booktitle = "Advances in Knowledge Discovery and Data Mining - 10th Pacific-Asia Conference, PAKDD 2006, Proceedings",
address = "德国",
note = "10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2006 ; Conference date: 09-04-2006 Through 12-04-2006",
}