Title :
Research on selecting initial points for k-means clustering
Author :
Wang, Shou-Qiang ; Zhu, Da-Ming
Author_Institution :
Sch. of Comput. Sci. & Technol., Shandong Univ., Jinan
Abstract :
Clustering analysis is one of the important problems in the fields of data mining and machine learning. There are many different clustering methods. Among them, k-means clustering is one of the most popular schemes owing to its simple and practicality. This paper investigates the approximate algorithm for the k-means clustering by means of selecting the k initial points from the input point set. An expected 2-approximation algorithm is presented in this paper. Meanwhile, an efficient algorithm for selecting the initial points is also proposed. At last some experimental results are given to test the valid of these algorithms.
Keywords :
approximation theory; pattern clustering; approximate algorithm; initial point selection; k-means clustering; Application software; Clustering algorithms; Clustering methods; Computer science; Data engineering; Data mining; Information analysis; Machine learning; Machine learning algorithms; Testing; Clustering; Randomized algorithm; k-means clustering;
Conference_Titel :
Machine Learning and Cybernetics, 2008 International Conference on
Conference_Location :
Kunming
Print_ISBN :
978-1-4244-2095-7
Electronic_ISBN :
978-1-4244-2096-4
DOI :
10.1109/ICMLC.2008.4620860
