Common clustering algorithms

Lee, Ickjai, and Yang, Jianhua (2020) Common clustering algorithms. In: Browen, Steven, Tauler, Roma, and Walczak, Beata, (eds.) Comprehensive Chemometrics. Elsevier, pp. 531-564.

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Abstract

The major steps of an overall clustering task are preclustering, clustering, and postclustering. Preclustering involves data preparation, including feature extraction, selection, transformation normalization, cleaning, and data reduction, whereas postclustering involves cluster usability encompassing cluster validity, reasoning, interpretation, and visualization. This article focuses on the second step, “clustering,” which is further divided into two key modules: clustering criterion and clustering method. This clustering step takes a set X = {x1, x2, …, xn} of preprocessed points (synonymously elements, objects, instances, cases or patterns) as an input and produces a clustered result as an output (either partitioning or hierarchical) for postclustering. It first requires a clustering criterion to be built and needs a clustering algorithm to optimize the clustering criterion.

Item ID: 68127
Item Type: Book Chapter (Later Edition)
ISBN: 978-0-444-64166-3
Copyright Information: Copyright © 2020 Elsevier B.V. All rights reserved.
Date Deposited: 08 Jun 2021 00:55
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460502 Data mining and knowledge discovery @ 100%
SEO Codes: 89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890299 Computer Software and Services not elsewhere classified @ 100%
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