Robust fuzzy clustering of relational data

In this paper, an entropy-regularized fuzzy clustering approach for non-Euclidean relational data and indefinite kernel data is developed that has not previously been discussed. It is important because relational data and kernel data are not always Euclidean and positive semi-definite, respectively. It is theoretically determined that an entropy-regularized approach for both non-Euclidean relational data and indefinite kernel data can be applied without using a β-spread transformation, and that two other options make the clustering results crisp for both data types. These results are in contrast to those from the standard approach. Numerical experiments are employed to verify the theoretical results, and the clustering accuracy of three entropy-regularized approaches for non-Euclidean relational data, and three for indefinite kernel data, is compared.

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Evolutionary fuzzy clustering of relational data

Theoretical Computer Science ◽

10.1016/j.tcs.2011.05.039 ◽

2011 ◽

Vol 412 (42) ◽

pp. 5854-5870 ◽

Cited By ~ 11

Author(s):

Danilo Horta ◽

Ivan C. de Andrade ◽

Ricardo J.G.B. Campello

Keyword(s):

Fuzzy Clustering ◽

Relational Data

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FCMdd-type linear fuzzy clustering for incomplete non-Euclidean relational data

2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011) ◽

10.1109/fuzzy.2011.6007379 ◽

2011 ◽

Cited By ~ 1

Author(s):

Takeshi Yamamoto ◽

Katsuhiro Honda ◽

Akira Notsu ◽

Hidetomo Ichihashi

Keyword(s):

Fuzzy Clustering ◽

Relational Data

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FNM-based and RFCM-based fuzzy clustering for tri-relational data

The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems ◽

10.1109/scis-isis.2012.6505050 ◽

2012 ◽

Author(s):

Yuchi Kanzawa

Keyword(s):

Fuzzy Clustering ◽

Relational Data

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Fuzzy Clustering of Large Relational Bioinformatics Datasets

Scalable Fuzzy Algorithms for Data Management and Analysis ◽

10.4018/978-1-60566-858-1.ch016 ◽

2010 ◽

pp. 379-399

Author(s):

Mihail Popescu

Keyword(s):

Amino Acid ◽

Amino Acid Sequence ◽

Medical Informatics ◽

Fuzzy Clustering ◽

Human Gene ◽

Relational Data ◽

Gene Products

In this chapter the author presents a fuzzy clustering methodology that can be employed for large relational datasets. Relational data is an N×N matrix that consists of pair-wise dissimilarities among N objects. Large relational datasets are encountered in many domains such as psychology or medical informatics, but they are abundant in bioinformatics where gene products are compared to each other based on various characteristics such as DNA or amino acid sequence. The fuzzy clustering methodology is exemplified on a set of about 30,000 human gene products.

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