Semi-fuzzy splitting in Online Divisive-Agglomerative Clustering

• Type: Chapter or Part of a Book
• Date: 2007

Authors

Pedro Rodrigues (Author) Other; João Gama (Author) FEP

Book

Title: Progress in Artificial Intelligence

Pages: 133-144

Indexing

ISI Proceedings

ISI Web of Knowledge - 7 Citations

Scopus - 9 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

CORDIS: Physical sciences > Computer science

Other information

Authenticus ID: P-004-EF4

DOI: 10.1007/978-3-540-77002-2_12

Abstract (EN): The Online Divisive-Agglomerative Clustering (ODAC) is an incremental approach for clustering streaming time series using a hierarchical procedure over time. It constructs a tree-like hierarchy of clusters of streams, using a top-down strategy based on the correlation between streams. The system also possesses an agglomerative phase to enhance a dynamic behavior capable of structural change detection. However, the split decision used in the algorithm focus on the crisp boundary between two groups, which implies a high risk since it has to decide based on only a small subset of the entire data. In this work we propose a semi-fuzzy approach to the assignment of variables to newly created clusters, for a better trade-off between validity and performance. Experimental work supports the benefits of our approach.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12