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Histogram-based fuzzy clustering and its comparison to fuzzy c-Means clustering in one-dimensional data
Book chapter

Histogram-based fuzzy clustering and its comparison to fuzzy c-Means clustering in one-dimensional data

A. Chong, T.D. Gedeon and K.W. Wong
Advances in Soft Computing Volume 14: Hybrid Information Systems, pp.253-267
Physica-Verlag HD
2002
DOI: https://doi.org/10.1007/978-3-7908-1782-9_19
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Abstract

In this paper, a histogram fuzzy clustering technique (HFC) has been proposed. The technique is designed specifically for one-dimensional data clustering. HFC is composed of two main components: trapezoidal cluster approximation, and bin width determination. By conducting experiments on two sets of real-world petroleum data, the effectiveness of HFC and the Fuzzy c-Means Clustering technique has been compared. It is found that the performance of HFC and FCMC varies across different sets of data. In some data, HFC can perform significantly better than FCMC. It is concluded that despite its limitation to clustering only one-dimensional data, HFC can be very useful in the rules extraction problem domain, especially in clustering the output space, which can be assumed to be always one-dimensional. Several other advantages of HFC, namely the technique is straightforward and computational efficient, are also discussed in the paper

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