In the last years the availability of huge transactional and experimental data sets and the arising requirements for data mining created needs for clustering algorithms that scale and can be applied in diverse domains. Thus, a variety of algorithms have been proposed which have application in different fields and may result in different partitioning of a data set, depending on the specific clus...

The validation of the results obtained by clustering algorithms is a fundamental part of the clustering process. The most used approaches for cluster validation are based on internal cluster validity indices. Although many indices have been proposed, there is no recent extensive comparative study of their performance. In this paper we show the results of an experimental work that compares 30 cl...

A microbial biofilm is structured mainly by a protective sticky matrix of extracellular polymeric substances. The appreciation of such structures is useful for the microbiologist and can be subjective to the observer. Thus, quantifying the underlying images in useful parameters by means of an objective image segmentation process helps substantially to reduce errors in quantification. This paper...

This paper is a survey of fuzzy set theory applied in cluster analysis. These fuzzy clustering algorithms have been widely studied and applied in a variety of substantive areas. They also become the major techniques in cluster analysis. In this paper, we give a survey of fuzzy clustering in three categories. The first category is the fuzzy clustering based on fuzzy relation. The second one is t...

In this paper, a new method of representing two-dimensional shapes using fuzzy-symbolic features and a similarity measure defined over fuzzy-symbolic features useful for clustering shapes is proposed. A k-mutual nearest neighborhood approach for clustering two-dimensional shapes is presented. The proposed shape representation scheme is invariant to similarity transformations and the clustering ...

Hierarchical conceptual clustering has been proven to be a useful data mining technique. Graphbased representation of structural information has been shown to be successful in knowledge discovery. The Subdue substructure discovery system provides the advantages of both approaches. In this paper we present Subdue and focus on its clustering capabilities. We use two examples to illustrate the val...

This paper focuses on estimating clustering validity by using logistic regression. For many applications it might be important to estimate the quality of the clustering, e.g. in case of speech segments’ clustering, make a decision whether to use the clustered data for speaker verification. In the case of short segments speakers clustering, the common criteria for cluster validity are average cl...

This paper presents an evaluation of a common approach that has been considered as a promising option for exploratory fMRI data analyses. The approach includes two stages: creating from the data a sequence of partitions with increasing number of subsets (clustering) and selecting the one partition in this sequence that exhibits the clearest indications of an existing structure (cluster validati...

Mixture models have been widely used in cluster analysis. Traditional mixture densities-based clustering algorithms usually predefine the number of clusters via random selection or contend based knowledge. An improper pre-selection of the number of clusters may easily lead to bad clustering outcome. Expectation-maximization (EM) algorithm is a common approach to estimate the parameters of mixtu...