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Survey of clustering data mining techniques (2002)

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by Pavel Berkhin
Citations:407 - 0 self
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BibTeX

@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   

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