A fuzzy clustering algorithm for symbolic interval data based on a single adaptive euclidean distance

Authors:
Francisco de A.T. de Carvalho
Affiliations:
Centro de Informatica - CIn/UFPE, Cidade Universitaria, Recife-PE, Brazil
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Year:
2006

Citing 6
Cited 0

Data clustering: a review

ACM Computing Surveys (CSUR)
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data

Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data
Clustering of interval data based on city-block distances

Pattern Recognition Letters
Adaptive Hausdorff distances and dynamic clustering of symbolic interval data

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Fuzzy clustering for symbolic data

IEEE Transactions on Fuzzy Systems

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Abstract

The recording of symbolic interval data has become a common practice with the recent advances in database technologies. This paper presents a fuzzy c-means clustering algorithm for symbolic interval data. This method furnishes a partition of the input data and a corresponding prototype (a vector of intervals) for each class by optimizing an adequacy criterion which is based on a suitable single adaptive Euclidean distance between vectors of intervals. Experiments with real and synthetic symbolic interval data sets showed the usefulness of the proposed method.