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Objective function‐based clustering
Author(s) -
Hall Lawrence O.
Publication year - 2012
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1059
Subject(s) - cluster analysis , computer science , correlation clustering , cure data clustering algorithm , data mining , clustering high dimensional data , canopy clustering algorithm , homogeneous , single linkage clustering , constrained clustering , partition (number theory) , consensus clustering , determining the number of clusters in a data set , data stream clustering , set (abstract data type) , machine learning , mathematics , combinatorics , programming language
Clustering is typically applied for data exploration when there are no or very few labeled data available. The goal is to find groups or clusters of like data. The clusters will be of interest to the person applying the algorithm. An objective function‐based clustering algorithm tries to minimize (or maximize) a function such that the clusters that are obtained when the minimum/maximum is reached are homogeneous. One needs to choose a good set of features and the appropriate number of clusters to generate a good partition of the data into maximally homogeneous groups. Objective functions for clustering are introduced. Clustering algorithms generated from the given objective functions are shown, with a number of examples of widely used approaches discussed. © 2012 Wiley Periodicals, Inc. This article is categorized under: Algorithmic Development > Scalable Statistical Methods Algorithmic Development > Structure Discovery Technologies > Machine Learning Technologies > Structure Discovery and Clustering