Clustering is fundamental for using big data. However, AP (affinity propagation) is not good at non-convex datasets …, and the input parameter has a marked impact on DBSCAN (density-based spatial clustering of applications with noise … obtains a group of normalized density from the AP clustering. The estimated parameters are monotonically. Then, the density is …

This article describes how the most widely used clustering, k-means, is prone to fall into a local optimum. Notably …, traditional clustering approaches are directly performed on private data and fail to cope with malicious attacks in massive data … as leaks through system resources and clustering outputs. To address these issues, the authors propose an efficient …

apply clustering techniques to data collected in real-time about readmitted patients in Intensive Care Units in order to …

-means clustering in Ganga River Basin Management and real-world feature data for detecting diabetes patients suffering from diabetes …

-Means standard methods are improved through fuzzy clustering at different image resolution levels by propagating fuzzy membership … fuzzy clustering with multi-resolution images is to avoid pixel misclassification according to the spatial cluster of the … better analysis. The results obtained after multi-resolution clustering are giving satisfactory results by comparing this …

, this article presents an informed sampling method that is based on a clustering approach for the prediction of seismic …

, processing of sequential data is of utmost importance. Because of the presence of imprecision intelligent clustering approaches … introduced which are more general and applicable to real world problems. In this paper, covering rough set based clustering …

feature subset selection to obtain significant features. Further, Fuzzy C-Means clustering is applied to restrict the search …