Abstract— Clustering is a powerful technique of data mining for extracting useful information from a set of data and classifies the data into several clusters based on similarity of the pattern. This paper presents the quality estimation for students’ projects data based on hierarchical clustering and fuzzy clustering using Min-Max method. From the experimental results it is seen the fuzzy clus...

Ensemble clustering is a novel research field that extends to unsupervised learning the approach originally developed for classification and supervised learning problems. In particular ensemble clustering methods have been developed to improve the robustness and accuracy of clustering algorithms, as well as the ability to capture the structure of complex data. In many clustering applications an...

Challenged by real-world clustering problems this paper proposes a novel fuzzy clustering scheme of datasets produced in the context of intuitionistic fuzzy set theory. More specifically, we introduce a variant of the Fuzzy C-Means (FCM) clustering algorithm that copes with uncertainty and a similarity measure between intuitionistic fuzzy sets, which is appropriately integrated in the clusterin...

Challenged by real-world clustering problems this paper proposes a novel fuzzy clustering scheme of datasets produced in the context of intuitionistic fuzzy set theory. More speci_cally, we introduce a variant of the Fuzzy C-Means (FCM) clustering algorithm that copes with uncertainty and a similarity measure between intuitionistic fuzzy sets, which is appropriately integrated in the clustering...

Clustering for the analysis of the gene expression profiles has been used for identifying the functions of the genes and of unknown genes. Since the genes usually belong to multiple functional families, fuzzy clustering methods are more appropriate than the conventional hard clustering methods. However, it is still required to devise natural way to measure the quality of the cluster partitions ...

In fuzzy clustering algorithms each object has a fuzzy membership associated with each cluster indicating the degree of association of the object to the cluster. Here we present a fuzzy subspace clustering algorithm, FSC, in which each dimension has a weight associated with each cluster indicating the degree of importance of the dimension to the cluster. Using fuzzy techniques for subspace clus...

In data mining, the conventional clustering algorithms have difficulties in handling the challenges posed by the collection of natural data which is often vague and uncertain. Fuzzy clustering methods have the potential to manage such situations efficiently. This paper introduces the limitations of conventional clustering methods through k-means and fuzzy c-means clustering and demonstrates the...

-Clustering algorithms are an integral part of both computational intelligence and pattern recognition. It is unsupervised methods for classifying data into subgroups with similarity based inter cluster and intra cluster. In fuzzy clustering algorithms, mainly used algorithm is Fuzzy c-means (FCM) algorithm. This FCM algorithm is efficient only for spherical data when the input of the data stru...

Aiming at fuzzy clustering with generalized entropy, a kernel fuzzy clustering algorithm with generalized entropy based on weighted sample is presented. By introducing weight of sample into objective function for fuzzy clustering with generalized entropy, we obtain optimization problem for fuzzy clustering with generalized entropy based on weighted sample. And we use Lagrange multiplier method ...

This paper presents the applications of hierarchical clustering to the generators in a power system. A useful application of fuzzy mathematics is that the correction of clustering results and determination of whether it can obtain correct transitive closure. Thus, the fuzzy transitive closure plays an important role in hierarchical clustering. Based on the fuzzy relation matrix, the hierarchica...