Data Clustering: 50 Years Beyond K-means

author: Anil K. Jain, Department of Computer Science and Engineering, Michigan State University
published: Oct. 10, 2008,   recorded: September 2008,   views: 3890
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Description

The practice of classifying objects according to perceived similarities is the basis for much of science. 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 in to taxonomic ranks: domain, kingdom, phylum, class, etc.). Cluster analysis is the formal study of algorithms and methods for grouping objects according to measured or perceived intrinsic characteristics. Cluster analysis does not use category labels that tag objects with prior identifiers, i.e., class labels. The absence of category information distinguishes cluster analysis (unsupervised learning) from discriminant analysis (supervised learning). The objective of cluster analysis is to simply find a convenient and valid organization of the data, not to establish rules for separating future data into categories. The development of clustering methodology has been a truly interdisciplinary endeavor. Taxonomists, social scientists, psychologists, biologists, statisticians, engineers, computer scientists, medical researchers, and others who collect and process real data have all contributed to clustering methodology. According to JSTOR, data clustering first appeared in the title of a 1954 article dealing with anthropological data. One of the most well-known, simplest and popular clustering algorithms is K-means. It was independently discovered by Steinhaus (1955), Lloyd (1957), Ball and Hall (1965) and McQueen (1967)! A search via Google Scholar found 22,000 entries with the word clustering and 1,560 entries with the words data clustering in 2007 alone. Among all the papers presented at CVPR, ECML, ICDM, ICML, NIPS and SDM in 2006 and 2007, 150 dealt with clustering. This vast literature speaks to the importance of clustering in machine learning, data mining and pattern recognition. A cluster is comprised of a number of similar objects grouped together. While it is easy to give a functional definition of a cluster, it is very difficult to give an operational definition of a cluster. This is because objects can be grouped into clusters with different purposes in mind. Data can reveal clusters of different shapes and sizes. Thus the crucial problem in identifying clusters in data is to specify or learn a similarity measure. In spite of thousands of clustering algorithms that have been published, a user still faces a dilemma regarding the choice of algorithm, distance metric, data normalization, number of clusters, and validation criteria. A familiarity with the application domain and clustering goals will certainly help in making an intelligent choice. This talk will provide background, discuss major challenges and key issues in designing clustering algorithms, summarize well known clustering methods, and point out some of the emerging research directions, including semi-supervised clustering that exploits pairwise constraints, ensemble clustering that combines results of multiple clusterings, learning distance metrics from side information, and simultaneous feature selection and clustering.

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Reviews and comments:

Comment1 Danish, November 20, 2008 at 1:27 p.m.:

better to put these lect on video website (youtub...etc) speed too bad..:(


Comment2 dr s natarajan, April 15, 2009 at 9 a.m.:

I do not have very good bandwidth. I will be thankful if a copy of this lecture is available in downloadable form
s.natarajan


Comment3 Heidar, April 27, 2009 at 9:14 p.m.:

I would be thankful if you put the link to download this video.


Comment4 Anbarasi, July 26, 2009 at 1:30 p.m.:

I am doing research to cluster Medical data with Predictive K-Means. Pl do the needful


Comment5 Majid, October 18, 2009 at 4:49 a.m.:

Heider, try Launch in a standalone WM Player and save it ..


Comment6 chiranjeevi, December 17, 2011 at 11:19 a.m.:

nice lecture

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