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Data Clustering: 50 Years Beyond K-Means (2008)
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BibTeX
@MISC{Jain08dataclustering:,
author = {Anil K. Jain},
title = {Data Clustering: 50 Years Beyond K-Means },
year = {2008}
}
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Abstract
Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms into taxonomic ranks: domain, kingdom, phylum, class, etc.). Cluster analysis is the formal study of algorithms and methods for grouping, or clustering, objects according to measured or perceived intrinsic characteristics or similarity. Cluster analysis does not use category labels that tag objects with prior identifiers, i.e., class labels. The absence of category information distinguishes data clustering (unsupervised learning) from classification or discriminant analysis (supervised learning). The aim of clustering is exploratory in nature to find structure in data. Clustering has a long and rich history in a variety of scientific fields. One of the most popular and simple clustering algorithms, K-means, was first published in 1955. In spite of the fact that K-means was proposed over 50 years ago and thousands of clustering algorithms have been published since then, K-means is still widely used. This speaks to the difficulty of designing a general purpose clustering algorithm and the illposed problem of clustering. We provide a brief overview of clustering, summarize well known clustering methods, discuss the major challenges and key issues in designing clustering algorithms, and point out some of the emerging and useful research directions, including semi-supervised clustering, ensemble clustering, simultaneous feature selection, and data clustering and large scale data clustering.
Keyphrases
data clustering cluster analysis unsupervised learning key issue semi-supervised clustering intrinsic characteristic discriminant analysis general purpose large scale data clustering taxonomic rank ensemble clustering useful research direction prior identifier sensible grouping scientific field class label rich history common scheme simultaneous feature selection category information distinguishes data clustering scientific classification fundamental mode category label major challenge formal study simple clustering algorithm illposed problem brief overview supervised learning
