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Survey of clustering data mining techniques (2002)
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@TECHREPORT{Berkhin02surveyof,
author = {Pavel Berkhin},
title = {Survey of clustering data mining techniques},
institution = {},
year = {2002}
}
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Abstract
Accrue Software, Inc. Clustering is a division of data into groups of similar objects. Representing the data by fewer clusters necessarily loses certain fine details, but achieves simplification. It models data by its clusters. Data modeling puts clustering in a historical perspective rooted in mathematics, statistics, and numerical analysis. From a machine learning perspective clusters correspond to hidden patterns, the search for clusters is unsupervised learning, and the resulting system represents a data concept. From a practical perspective clustering plays an outstanding role in data mining applications such as scientific data exploration, information retrieval and text mining, spatial database applications, Web analysis, CRM, marketing, medical diagnostics, computational biology, and many others. Clustering is the subject of active research in several fields such as statistics, pattern recognition, and machine learning. This survey focuses on clustering in data mining. Data mining adds to clustering the complications of very large datasets with very many attributes of different types. This imposes unique
Keyphrases
data mining technique data mining numerical analysis many attribute unsupervised learning outstanding role information retrieval accrue software computational biology spatial database application historical perspective large datasets many others model data text mining data modeling active research similar object scientific data exploration practical perspective clustering pattern recognition different type perspective cluster medical diagnostics machine learning data concept certain fine detail web analysis several field
