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WIREs Data Mining Knowl Discov
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Objective function‐based clustering

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Abstract 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

k‐Means clustering algorithm.

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An approach to speeding up k‐means (applied in step 2) with one pass through the data.

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Finding the right number of clusters with a partition validity metric. Any validity metric that applies to a particular objective function‐based clustering algorithm can be applied.

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Kernel‐based k‐means/FKM algorithm.

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Relational fuzzy k‐means clustering algorithm.

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Relational k‐means clustering algorithm.

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PKM algorithm.

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Three cluster problem that is difficult.

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The Iris data clustered by the EM algorithm.

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The EM clustering algorithm.

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FKM algorithm.

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The Iris data (a) with labels (b) clustered by k‐means with a Good initialization and (c) clustered by k‐means with a Bad initialization.

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Technologies > Machine Learning
Technologies > Structure Discovery and Clustering
Algorithmic Development > Scalable Statistical Methods
Algorithmic Development > Structure Discovery

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