The partitional clustering concept started with K-means algorithm which was published in
1957. Since then many classical partitional clustering algorithms have been reported based …

Nature has always been a source of inspiration. Over the last few decades, it has stimulated
many successful algorithms and computational tools for dealing with complex and …

Ant-based clustering is a biologically inspired data clustering technique. Clustering task
aims at the unsupervised classification of patterns in different groups. Clustering problem …

Over the last several years, DBSCAN (Density-Based Spatial Clustering of Applications with
Noise) has been widely used in many areas of science due to its simplicity and the ability to …

Swarm intelligence is a relatively novel field. It addresses the study of the collective
behaviors of systems made by many components that coordinate using decentralized …

The density-based spatial clustering of applications with noise (DBSCAN) is regarded as a
pioneering algorithm of the density-based clustering technique. It provides the ability to …

Recent years have witnessed a trend of monitoring human respiration using Channel State
Information (CSI) retrieved from commodity WiFi devices. Existing approaches essentially …

We propose an efficient hybrid algorithm, known as ACOSS, for solving resource-
constrained project scheduling problems (RCPSP) in real-time. The ACOSS algorithm …

In this work we consider spatial clustering problem with no a priori information. The number
of clusters is unknown, and clusters may have arbitrary shapes and density differences. The …

Hierarchical routing is an efficient way to lower energy consumption within a cluster. Due to
the characteristics of wireless channels, multi-hop communications between a data source …