Efficient clustering of web-derived data sets

• Type: Article in International Conference Proceedings Book
• Date: 2009

Authors

Luís Sarmento (Author) Other; Alexander Kehlenbeck (Author) Other; Eugénio Oliveira (Author) FEUP; Lyle Ungar (Author) Other

Conference proceedings International

Title: Machine Learning and Data Mining in Pattern Recognition

Pages: 398-412

6th International Conference on Machine Learning and Data Mining in Pattern Recognition (MLDM 2009)

Leipzig, Germany, 23-25 July, 2009

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 0 Citations

INSPEC

Scientific classification

FOS: Natural sciences > Computer and information sciences

CORDIS: Physical sciences > Computer science > Informatics

Other information

Authenticus ID: P-003-R7P

DOI: 10.1007/978-3-642-03070-3_30

Abstract (EN): Many data sets derived from the web are large, high-dimensional, sparse and have a Zipfian distribution of both classes and features. On such data sets, current scalable clustering methods such as streaming clustering suffer from fragmentation. where large classes are incorrectly divided into many smaller clusters. and computational efficiency drops significantly. We present a new clustering algorithm based on connected components that addresses these issues and so works well oil web-type data.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15