Clustering is the process of analyzing data to find clusters of data objects that are similar in some sense to one … another. Some research studies have also extended the usage of clustering concept in inventory management. Yet, not many … research studies have considered the application of clustering approach on determining both optimal order quantity and loss …

introduces a new hierarchical semi-supervised clustering method based on ordinal density. The results of carried out experiments …

is based mostly on three cluster techniques like; K means, Fuzzy c-means and hierarchical clustering. The authors used … evolutionary techniques like genetic algorithms (GA) to extend the performance of the clustering model. The performance of these … obtained by the K-means clustering technique, 96.50%; whereas, Fuzzy C-means clustering received 93.50% and hierarchical …

data analysis and predictive modeling of information systems. Firstly, they apply clustering to study the information …

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 …