# A Bimodality Test in High Dimensions

Serdica Journal of Computing (2012)

- Volume: 6, Issue: 4, page 437-450
- ISSN: 1312-6555

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topPalejev, Dean. "A Bimodality Test in High Dimensions." Serdica Journal of Computing 6.4 (2012): 437-450. <http://eudml.org/doc/250976>.

@article{Palejev2012,

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.},

author = {Palejev, Dean},

journal = {Serdica Journal of Computing},

keywords = {Clustering; Bimodality; Multidimensional Space; Asymptotic Test; clustering; bimodality; multidimensional space; asymptotic test},

language = {eng},

number = {4},

pages = {437-450},

publisher = {Institute of Mathematics and Informatics Bulgarian Academy of Sciences},

title = {A Bimodality Test in High Dimensions},

url = {http://eudml.org/doc/250976},

volume = {6},

year = {2012},

}

TY - JOUR

AU - Palejev, Dean

TI - A Bimodality Test in High Dimensions

JO - Serdica Journal of Computing

PY - 2012

PB - Institute of Mathematics and Informatics Bulgarian Academy of Sciences

VL - 6

IS - 4

SP - 437

EP - 450

AB - 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.

LA - eng

KW - Clustering; Bimodality; Multidimensional Space; Asymptotic Test; clustering; bimodality; multidimensional space; asymptotic test

UR - http://eudml.org/doc/250976

ER -

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