@inproceedings{887e18f8b43a4ec8a01517d0db144ebc,
title = "An expanding clustering algorithm based on density searching",
abstract = "Most clustering algorithms need to preset the initial parameters which affect the performance of clustering very much. To solve this problem, a new method is proposed, which determine the center points of clustering by density-searching according to the universality of the Gaussian distribution. After the center was obtained, the cluster expands based on the correlation coefficient between clusters and the membership of the samples until the terminating condition is met. The experimental results show that this method could classify the samples of Gaussian distribution with different degree of overlap accurately. Compared with the fuzzy c-means algorithm, the proposed method is more accurate and timesaving when applied to the Iris data and Fossil data.",
keywords = "Algorithm, clustering, clustering center, density searching",
author = "Liguo Tan and Yang Liu and Xinglin Chen",
year = "2011",
doi = "10.1007/978-3-642-24097-3\_19",
language = "英语",
isbn = "9783642240966",
series = "Communications in Computer and Information Science",
number = "PART 6",
pages = "110--116",
booktitle = "Information and Management Engineering - International Conference, ICCIC 2011, Proceedings",
edition = "PART 6",
note = "2011 International Conference on Computing, Information and Control, ICCIC 2011 ; Conference date: 17-09-2011 Through 18-09-2011",
}