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Kybernetika 34(4):375-380, 1998.

Detecting a Data Set Structure Through the Use of Nonlinear Projections Search and Optimization.

Victor L. Brailovsky and Michael Har-Even


Abstract:

Detecting a cluster structure is considered. This means solving either the problem of discovering a natural decomposition of data points into groups (clusters) or the problem of detecting clouds of data points of a specific form. In this paper both these problems are considered. To discover a cluster structure of a specific arrangement or a cloud of data of a specific form a class of nonlinear projections is introduced. Fitness functions that estimate to what extent a given subset of data points (in the form of the corresponding projection) represents a good solution for the first or the second problem are presented. To find a good solution one uses a search and optimization procedure in the form of Evolutionary Programming. The problems of cluster validity and robustness of algorithms are considered. Examples of applications are discussed.


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BIB TeX

@article{kyb:1998:4:375-380,

author = {Brailovsky, Victor L. and Har-Even, Mich\ael},

title = {Detecting a Data Set Structure Through the Use of Nonlinear Projections Search and Optimization.},

journal = {Kybernetika},

volume = {34},

year = {1998},

number = {4},

pages = {375-380}

publisher = {{\'U}TIA, AV {\v C}R, Prague },

}


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