Principal methods in nonhierarchical and hierarchical fuzzy clustering are overviewed. In particular, the method of fuzzy c-means is focused upon and recent algorithms in fuzzy c-means are described. It is shown that the concept of regularization plays an important role in the fuzzy c-means. Classification functions induced from fuzzy clustering are discussed and variations of the standard fuzz...

In clustering line segments into a straight line, threshold-based methods such as hierarchical clustering are often used. The line segments comprising a straight line often get misaligned due to noise. Thresholdbased methods have di culty clustering such line segments. A new cluster extraction method is proposed to cope with this problem. This method extracts fuzzy clusters one by one using mat...

Some methods of fuzzy clustering need to use a priori knowledge about the number of fuzzy classes or some other information about the possible distribution of the clusters. A way to improve these methods is to use hierarchical clustering as a preprocessing of the data. This approach does not provide a simple partition of the data set, but a hierarchy of them. In this paper we define several mea...

In this paper, a method of hierarchical clustering and a selection of fuzzy classification algorithms are applied successively to the data set that contains measured characteristics of folk verses collected from 104 historical parishes of Estonia. The aim of the study is to detect the groups of parishes that are similar in terms of folk verse characteristics and to give us insight into the reas...

E-service evaluation is a complex problem in which many qualitative attributes must be considered. These kinds of attributes make the evaluation process hard and vague. Cost–benefit analyses applied to various areas are usually based on the data under certainty or risk. In case of uncertain, vague, and/or linguistic data, the fuzzy set theory can be used to handle the analysis. In this article,...

Hierarchical Clustering is a procedure of cluster analysis which aims to construct a hierarchy of clusters. There are two kinds of hierarchical clustering i.e. Agglomerative, which is a bottom – up approach, where all the observations start in its own cluster, and pairs of clusters are merged moving up the hierarchy, and the other one is divisive, which is a top down approach, where each observ...

Problem statement: Malignant melanoma is the most frequent type of skin cancer. Its incidence has been rapidly increasing over the last few decades. Medical image segmentation is the most essential and crucial process in order to facilitate the characterization and visualization of the structure of interest in medical images. Approach: This study explains the task of segmenting skin lesions in ...

A decomposition scheme for 3D fuzzy objects is presented. The decomposition is guided by a fuzzy distance transform (FDT) of the fuzzy object and aims to decompose the fuzzy object into simpler parts. Relevant voxels, corresponding to the ‘‘centres’’ of the parts, are detected on the FDT and suitably grouped, using a hierarchical clustering technique, into significant seeds for the decompositio...

A new type of hierarchical fuzzy system (HFS), namely, Hierarchical Classifying-Type Fuzzy System (HCTFS), is developed and proposed in the paper. While the HCTFS enjoys the full benefits of a traditional HFS, one of which is to suppress the effects of the unwanted phenomenon, “the curse of dimensionality,” it also offers one great advantage that all rule strengths are preserved when passing th...

The evaluation algorithm is based on the attributes of data objects. There is a certain correlation between attributes, and attributes are divided into key attributes and secondary attributes. The evaluation from the data objects in a hierarchical design based on key attributes can reduce the data size and algorithm complexity, and without prejudice on the basis of evaluation results can improv...