Probabilistic clustering of interval data

• Type: Article in International Scientific Journal
• Date: 2015

Authors

P. Brito (Author) FEP; A.P. Duarte Silva (Author) FEP; José G. Dias (Author) Other

Journal

Title: Intelligent Data Analysis

Vol. 19

Pages: 293-313

ISSN: 1088-467X

Publisher: IOS PRESS

Indexing

ISI Web of Knowledge - 6 Citations

Scopus - 9 Citations

Scientific classification

FOS: Natural sciences > Mathematics

CORDIS: Physical sciences > Mathematics > Statistics

Other information

Authenticus ID: P-00A-CFZ

DOI: 10.3233/ida-150718

Abstract (EN): In this paper we address the problem of clustering interval data, adopting a model-based approach. To this purpose, parametric models for interval-valued variables are used which consider configurations for the variance-covariance matrix that take the nature of the interval data directly into account. Results, both on synthetic and empirical data, clearly show the well-founding of the proposed approach. The method succeeds in finding parsimonious heterocedastic models which is a critical feature in many applications. Furthermore, the analysis of the different data sets made clear the need to explicitly consider the intrinsic variability present in interval data.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 21