In this paper, a prototype-based supervised clustering algorithm is proposed. The proposed algorithm, called the Supervised Growing Neural Gas algorithm (SGNG), incorporates several techniques from some unsupervised GNG algorithms such as the adaptive learning rates and the cluster repulsion mechanisms of the Robust Growing Neural Gas algorithm, and the Type Two Learning Vector Quantization (LV...

I n t r o d u c t i o n The word "clustering" (unsupervised classification) refers to methods of grouping objects based on some similarity measure between them. Clustering algorithms can be classified into four classes, namely Partitional, Hierarchical, Density-based and Grid-based [8]. Each of these classes has subclasses and different corresponding approaches, e.g., conceptual, fuzzy, selforg...

The evaluation of clustering algorithms is intrinsically difficult because of the lack of objective measures. Since the evaluation of clustering algorithms normally involves multiple criteria, it can be modeled as a multiple criteria decision making (MCDM) problem. This paper presents an MCDM-based approach to rank a selection of popular clustering algorithms in the domain of financial risk ana...

This study develops novel approaches to partition mixed data into natural groups, that is, clustering datasets containing both numeric and nominal attributes. Such data arises in many diverse applications. Our approach addresses two important issues regarding clustering mixed datasets. One is how to find the optimal number of clusters which is important because this is unknown in many applicati...

This dissertation studies the problem of clustering objects represented by relational data. This is a pertinent problem as many real-world data sets can only be represented by relational data for which object-based clustering algorithms are not designed. Relational data are encountered in many fields including biology, management, industrial engineering, and social sciences. Unlike numerical ob...

The explosive growth of World Wide Web (WWW) has necessitated the development of Web personalization systems in order to understand the user preferences to dynamically serve customized content to individual users. To reveal information about user preferences from Web usage data, Web Usage Mining (WUM) techniques are extensively being applied to the Web log data. Clustering techniques are widely...

This paper discusses the relationships between indiscernibility degree and clustering results in rough clustering. We first examine the relationship between the threshold value of indiscernibility degree and resultant clusters. After that, we apply random disturbance to the perfect relations, and examine how the result changes. The results implies the threshold-validity curve may have globally ...

A review of some popular fuzzy cluster validity indices is given. An index that is based on the generalization of silhouettes to fuzzy partitions is compared with the reviewed indices in conjunction with fuzzy c-means clustering.

An overview of fuzzy c-means clustering algorithms is given where we focus on different objective functions: they use regularized dissimilarity, entropy-based function, and function for possibilistic clustering. Classification functions for the objective functions and their properties are studied. Fuzzy c-means algorithms using kernel functions is also discussed with kernelized cluster validity...

This paper examines the problem of clustering a sequence of objects that cannot be described with a predeened list of attributes (or variables). In many applications, such a crisp representation cannot be determined. An extension of the traditionnal propositionnal formalism is thus proposed, which allows objects to be represented as a set of components. The algorithm used for clustering is brie...