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Projective Clustering

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Encyclopedia of Machine Learning and Data Mining

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Synonyms

Local feature selection; Subspace clustering

Definition

Projective clustering is a class of problems in which the input consists of high-dimensional data, and the goal is to discover those subsets of the input that are strongly correlated in subspaces of the original space. Each subset of correlated points, together with its associated subspace, defines a projective cluster. Thus, although all cluster points are close to each other when projected on the associated subspace, they may be spread out in the full-dimensional space. This makes projective clustering algorithms particularly useful when mining or indexing datasets for which full-dimensional clustering is inadequate (as is the case for most high-dimensional inputs). Moreover, such algorithms compute projective clusters that exist in different subspaces, making them more general than global dimensionality-reduction techniques.

Motivation and Background

Projective clustering is a type of data mining whose main motivation...

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Recommended Reading

  • Agarwal PK, Mustafa N (2004) k-means projective clustering. In: Proceeding of ACM SIGMOD-SIGACT-SIGART symposium principles of database systems, pp 155–165

    Google Scholar 

  • Agarwal PK, Procopiuc CM (2003) Approximation algorithms for projective clustering. J Algorithms 46(2):115–139

    Article  MathSciNet  MATH  Google Scholar 

  • Agarwal PK, Har-Peled S, Varadarajan KR (2005) Geometric approximation via coresets. In: Goodman JE, Pach J, Welzl E (eds) Combinatorial and computational geometry. Cambridge University Press, Cambridge/New York, pp 1–30

    Google Scholar 

  • Aggarwal CC, Yu PS (2000) Finding generalized projected clusters in high dimensional spaces. In: Proceeding of ACM SIGMOD international conference management of data, pp 70–81

    Google Scholar 

  • Aggarwal CC, Procopiuc CM, Wolf JL, Yu PS, Park JS (1999) Fast algorithms for projected clustering. In: Proceeding of ACM SIGMOD international conference management of data, pp 61–72

    Google Scholar 

  • Agrawal R, Gehrke J, Gunopulos D, Raghavan P (1998) Automatic subspace clustering of high dimensional data for data mining applications. In: Proceeding of ACM SIGMOD international conference management of data, pp 94–105

    Google Scholar 

  • Beyer K, Goldstein J, Ramakrishnan R, Shaft U (1999) When is “nearest neighbour” meaningful? In: Proceeding of 7th international conference data theory, vol 1540, pp 217–235

    Google Scholar 

  • Böhm C, Kailing K, Kröger P, Zimek A (2004) Computing clusters of correlation connected objects. In: Proceeding of ACM SIGMOD international conference management of data, pp 455–466

    Google Scholar 

  • Chakrabarti K, Mehrotra S (2000) Local dimensionality reduction: a new approach to indexing high dimensional spaces. In: Proceeding of 26th international conference very large data bases, pp 89–100

    Google Scholar 

  • Hinneburg A, Keim DA (1999) Optimal grid-clustering: towards breaking the curse of dimensionality in high-dimensional clustering. In: Proceeding of 25th international conference very large data bases, pp 506–517

    Google Scholar 

  • Li T, Ma S, Ogihara M (2004) Document clustering via adaptive subspace iteration. In: Proceeding of 27th international ACM SIGIR conference research and development in information retrieval, pp 218–225

    Google Scholar 

  • Liu J, Wang W (2003) Op-cluster: clustering by tendency in high dimensional space. In: Proceeding of international conference on data mining, pp 187–194

    Google Scholar 

  • Megiddo N, Tamir A (1982) On the complexity of locating linear facilities in the plane. Oper Res Lett 1:194–197

    Article  MathSciNet  MATH  Google Scholar 

  • Parsons L, Haque E, Liu H (2004) Subspace clustering for high dimensional data: a review. ACM SIGKDD Explor Newslett 6(1):90–105

    Article  Google Scholar 

  • Procopiuc CM, Jones M, Agarwal PK, Murali TM (2002) A Monte Carlo algorithm for fast projective clustering. In: Proceeding of ACM SIGMOD international conference management of data, pp 418–427

    Google Scholar 

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Author information

Authors and Affiliations

  1. AT&T Labs, Florham Park, NJ, USA

    Cecilia M. Procopiuc

Authors
  1. Cecilia M. Procopiuc
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Editor information

Editors and Affiliations

  1. The University of New South Wales, Sydney, NSW, Australia

    Claude Sammut

  2. Faculty of Information Technology, Monash University, Melbourne, VIC, Australia

    Geoffrey I. Webb

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Procopiuc, C.M. (2017). Projective Clustering. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_676

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