Fuzzy C-mean (FCM) is the most well-known and widely-used fuzzy clustering algorithm. However, one of the weaknesses of the FCM is the way it assigns membership degrees to data which is based on the distance to the cluster centers. Unfortunately, the membership degrees are determined without considering the shape and density of the clusters. In this paper, we propose an algorithm which takes th...

Abstract The idea of the modal formulation density-based clustering is to associate groups with regions around modes probability density function underlying data. correspondence between clusters and dense in sample space here exploited discuss an extension this approach analysis social networks. Conceptually, notion high-density cluster fits well one community a network, regarded as collection ...

Hierarchical density-based clustering is a powerful tool for exploratory data analysis, which can play an important role in the understanding and organization of datasets. However, its applicability to large datasets limited because computational complexity hierarchical methods has quadratic lower bound number objects be clustered. MapReduce popular programming model speed up mining machine lea...

Cluster analysis is a crucial technique in unsupervised machine learning, pattern recognition, and data analysis. However, current clustering algorithms suffer from the need for manual determination of parameter values, low accuracy, inconsistent performance concerning size structure. To address these challenges, novel algorithm called fully automated density-based method (FADBC) proposed. The ...

As the discovery of information from text corpora becomes more and more important there is a necessity to develop clustering algorithms designed for such a task. One of the most, successful approach to clustering is the density based methods. However due to the very high dimensionality of the data, these algorithms are not directly applicable. In this paper we demonstrate the need to suitably e...