A two-stage genetic algorithm for automatic clustering
Neurocomputing
Decoding network activity from LFPs: a computational approach
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
On initializations for the minkowski weighted k-means
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
Scientific and Technical Information Processing
An empirical evaluation of different initializations on the number of k-means iterations
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Multimodal late fusion bag of features applied to scene detection
Proceedings of the 19th Brazilian symposium on Multimedia and the web
Online fuzzy medoid based clustering algorithms
Neurocomputing
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The issue of determining “the right number of clusters” in K-Means has attracted considerable interest, especially in the recent years. Cluster intermix appears to be a factor most affecting the clustering results. This paper proposes an experimental setting for comparison of different approaches at data generated from Gaussian clusters with the controlled parameters of between- and within-cluster spread to model cluster intermix. The setting allows for evaluating the centroid recovery on par with conventional evaluation of the cluster recovery. The subjects of our interest are two versions of the “intelligent” K-Means method, ik-Means, that find the “right” number of clusters by extracting “anomalous patterns” from the data one-by-one. We compare them with seven other methods, including Hartigan’s rule, averaged Silhouette width and Gap statistic, under different between- and within-cluster spread-shape conditions. There are several consistent patterns in the results of our experiments, such as that the right K is reproduced best by Hartigan’s rule – but not clusters or their centroids. This leads us to propose an adjusted version of iK-Means, which performs well in the current experiment setting.