Variable-Wise Kernel-Based Clustering Algorithms for Interval-Valued Data

This paper presents partitioning hard kernel clustering algorithms for interval-valued data based on adaptive distances. These adaptive distances are obtained as sums of squared Euclidean distances between interval-valued data computed individually for each interval-valued variable by means of kerne...

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Bibliographic Details
Main Authors: de A T de Carvalho, Francisco, Barbosa, Gibson B.N., Ferreira, Marcelo R.P.
Format: Conference Proceeding
Language:English
Subjects:
Adaptive distances
Cities and towns
Clustering
Clustering algorithms
Kernel
Kernel functions
Partitioning algorithms
Prototypes
Resource management
Vectors
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Summary:This paper presents partitioning hard kernel clustering algorithms for interval-valued data based on adaptive distances. These adaptive distances are obtained as sums of squared Euclidean distances between interval-valued data computed individually for each interval-valued variable by means of kernel functions. The advantage of the proposed approach over the conventional kernel clustering approaches for interval-valued data is that it allows to learn the relevance weights of the variables during the clustering process, improving the performance of the algorithms. Experiments with real interval-valued data sets show the usefulness of these kernel clustering algorithms.
ISSN:1522-4899
2375-0235
DOI:10.1109/SBRN.2012.21