An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Clustering of interval data based on city-block distances
Pattern Recognition Letters
Kernel k-means: spectral clustering and normalized cuts
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive Hausdorff distances and dynamic clustering of symbolic interval data
Pattern Recognition Letters
New clustering methods for interval data
Computational Statistics
Interval Data Clustering with Applications
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Fuzzy c-means clustering methods for symbolic interval data
Pattern Recognition Letters
A survey of kernel and spectral methods for clustering
Pattern Recognition
Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability)
Symbolic Data Analysis and the SODAS Software
Symbolic Data Analysis and the SODAS Software
Cluster Analysis
K-means clustering versus validation measures: a data-distribution perspective
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning assignment order of instances for the constrained K-means clustering algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Unsupervised pattern recognition models for mixed feature-type symbolic data
Pattern Recognition Letters
Dynamic clustering of interval-valued data based on adaptive quadratic distances
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Clustering evaluation in feature space
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
K-means Clustering for Symbolic Interval Data Based on Aggregated Kernel Functions
ICTAI '10 Proceedings of the 2010 22nd IEEE International Conference on Tools with Artificial Intelligence - Volume 02
A partitioning method for symbolic interval data based on kernelized metric
Proceedings of the 20th ACM international conference on Information and knowledge management
Kernel Discriminant Analysis Using Case-Specific Smoothing Parameters
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy clustering for symbolic data
IEEE Transactions on Fuzzy Systems
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
Clustering based on a near neighbor graph and a grid cell graph
Journal of Intelligent Information Systems
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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.