Please use this identifier to cite or link to this item: http://hdl.handle.net/10525/1977

 Title: A Bimodality Test in High Dimensions Authors: Palejev, Dean Keywords: ClusteringBimodalityMultidimensional SpaceAsymptotic Test Issue Date: 2012 Publisher: Institute of Mathematics and Informatics Bulgarian Academy of Sciences Citation: Serdica Journal of Computing, Vol. 6, No 4, (2012), 437p-450p Abstract: We present a test for identifying clusters in high dimensional data based on the k-means algorithm when the null hypothesis is spherical normal. We show that projection techniques used for evaluating validity of clusters may be misleading for such data. In particular, we demonstrate that increasingly well-separated clusters are identified as the dimensionality increases, when no such clusters exist. Furthermore, in a case of true bimodality, increasing the dimensionality makes identifying the correct clusters more difficult. In addition to the original conservative test, we propose a practical test with the same asymptotic behavior that performs well for a moderate number of points and moderate dimensionality. ACM Computing Classification System (1998): I.5.3. URI: http://hdl.handle.net/10525/1977 ISSN: 1312-6555 Appears in Collections: Volume 6 Number 4

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