CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Acceleration of K-Means and Related Clustering Algorithms
ALENEX '02 Revised Papers from the 4th International Workshop on Algorithm Engineering and Experiments
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability)
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This project shows the development of a new clustering algorithm, based on k-means, which faces its problems with clusters of differences variances. This new algorithm uses a line segment as prototype which captures the axis that presents the biggest variance of the cluster. The line segment adjusts iteratively its long and direction as the data are classified. To perform the classification, a border region that determines approximately the limit on the cluster is built based on geometric model, which depends on the central line segment. The data are classified later according to their proximity to the different border regions. The process is repeated until the parameters of the all border regions associated with each cluster remain constant.