A new algorithm for clustering life course trajectories is presented and tested with large register data. Life courses … clustering result for this kind of data provides interesting subgroups with similar life course trajectories. The high sampling … information. The new algorithm deals with this problem by simultaneously clustering and identifying critical junctures for each of …

Expectation Maximization (EM) is a widely employed mixture model-based data clustering algorithm and produces … clustering algorithms. This paper presents an algorithm for the novel hybridization of EM and K-Means techniques for achieving … better clustering performance (NovHbEMKM). This algorithm first performs K-Means and then using these results it performs EM …

authors propose a Mobility Aware Clustering Scheme (MACS), which organizes the IoT devices in clusters using a fitness …

one, this technique was designed to solve combinatorial optimization problems, and by embedding a clustering algorithm …

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 …