2014 ClusteringHighDimensionalDataEx

• (Kriegel & Ntoutsi, 2014) ⇒ Hans-Peter Kriegel, and Eirini Ntoutsi. (2014). “Clustering High Dimensional Data: Examining Differences and Commonalities Between Subspace Clustering and Text Clustering - a Position Paper.” In: ACM SIGKDD Explorations Newsletter Journal, 15(2). doi:10.1145/2641190.2641192

Subject Headings: High-Dimensional Data Clustering, Subspace Clustering, Text Clustering.

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Clustering+High+Dimensional+Data%3A+Examining+Differences+and+Commonalities+Between+Subspace+Clustering+and+Text+Clustering+-+a+Position+Paper
• http://dl.acm.org/citation.cfm?id=2641190.2641192&preflayout=flat#citedby

Quotes

Author Keywords

high dimensional data, subspace clustering, text clustering

Abstract

The goal of this position paper is to contribute to a clear understanding of the commonalities and differences between subspace clustering and text clustering. Often text data is foisted as an ideal fit for subspace clustering due to its high dimensional nature and sparsity of the data. Indeed, the areas of subspace clustering and text clustering share similar challenges and the same goal, the simultaneous extraction of both clusters and the dimensions where these clusters are defined. However, there are fundamental differences between the two areas w.r.t object feature representation, dimension weighting and incorporation of these weights in the dissimilarity computation. We make an attempt to bridge these two domains in order to facilitate the exchange of ideas and best practices between them.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 ClusteringHighDimensionalDataEx	Hans-Peter Kriegel Eirini Ntoutsi			Clustering High Dimensional Data: Examining Differences and Commonalities Between Subspace Clustering and Text Clustering - a Position Paper				10.1145/2641190.2641192		2014