Clustering interval data through kernel-induced feature space

Recently, kernel-based clustering in feature space has shown to perform better than conventional clustering methods in unsupervised classification. In this paper, a partitioning clustering method in kernel-induce feature space for symbolic interval-valued data is introduced. The distance between an...

Full description

Saved in:
Bibliographic Details
Published in:Journal of intelligent information systems 2013-02, Vol.40 (1), p.109-140
Main Authors: da Costa, Anderson F. B. F., Pimentel, Bruno A., de Souza, Renata M. C. R.
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Artificial Intelligence
Cities
Classification
Cluster analysis
Clustering
Clusters
Computer Science
Data analysis
Data Structures and Information Theory
Datasets
Discriminant analysis
Experiments
Information Storage and Retrieval
Information systems
Intelligent systems
Intervals
IT in Business
Kernels
Methods
Natural Language Processing (NLP)
Partitions
Pattern recognition
Studies
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Recently, kernel-based clustering in feature space has shown to perform better than conventional clustering methods in unsupervised classification. In this paper, a partitioning clustering method in kernel-induce feature space for symbolic interval-valued data is introduced. The distance between an item and its prototype in feature space is expanded using a two-component mixture kernel to handle intervals. Moreover, tools for the partition and cluster interpretation of interval-valued data in feature space are also presented. To show the effectiveness of the proposed method, experiments with real and synthetic interval data sets were performed and a study comparing the proposed method with different clustering algorithms of the literature is also presented. The clustering quality furnished by the methods is measured by an external cluster validity index (corrected Rand index). These experiments showed the usefulness of the kernel K-means method for interval-valued data and the merit of the partition and cluster interpretation tools.
ISSN:0925-9902
1573-7675
DOI:10.1007/s10844-012-0219-2