Algorithms for clustering data
Algorithms for clustering data
EM algorithms for PCA and SPCA
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Clustering Algorithms
Clustering For Data Mining: A Data Recovery Approach (Chapman & Hall/Crc Computer Science)
Clustering For Data Mining: A Data Recovery Approach (Chapman & Hall/Crc Computer Science)
Nearest neighbours in least-squares data imputation algorithms with different missing patterns
Computational Statistics & Data Analysis
Knowing a Good Show When You See One
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
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K-means is one of the most popular data mining and unsupervised learning algorithms that solve the well known clustering problem. The procedure follows a simple and easy way to classify a given data set through a pre-specified number of clusters K, therefore the problem of determining "the right number of clusters" has attracted considerable interest. However, to the authors' knowledge, no experimental results of their comparison have been reported so far. This paper presents results of such a comparison involving eight selection options presenting four approaches. We generate data according to a Gaussian-mixture distribution with clusters' spread and spatial sizes variant. Most consistent results are shown by the least squares and least modules version of an intelligent version of the method, iK-Means by Mirkin [14]. However, the right K is reproduced best by the Hartigan's [5] method. This leads us to propose an adjusted iK-Means method, which performs well in the current experiment setting.