Data clustering is a popular data analysis technique needed in many fields. Recent years, some swarm intelligence-based approaches for clustering were proposed and achieved encouraging results. This paper presents a Hybrid Artificial Bee Colony (HABC) algorithm for data clustering. The incentive mechanism of HABC is enhancing the information exchange (social learning) between bees by functions,...

Data clustering is a technique for clustering set of objects into known number of groups. Several approaches are widely applied to data clustering so that objects within the clusters are similar and objects in different clusters are far away from each other. K-Means, is one of the familiar center based clustering algorithms since implementation is very easy and fast convergence. However, K-Mean...

With game play data, empirical approaches to clustering are typically based solely on game outcomes, e.g. kills, deaths, and score for each player. In this paper, we investigate a method for clustering players based on how a player’s choices relate to outcomes, or equivalently the latent player styles exhibited by players. Our approach is based on a Bayesian semi-parametric clustering method wh...

1 Hybrid Clustering using Elitist Teaching Learning-Based Optimization: An Improved Hybrid Approach of TLBO D.P. Kanungo, Department of Computer Science Engineering and Information Technology, Veer Surendra Sai University of Technology (VSSUT), Odisha, India Janmenjoy Nayak, Department of Computer Science Engineering and Information Technology, Veer Surendra Sai University of Technology (VSSUT)...

The main goal of this paper is to compare the performance which can be achieved by five different approaches analyzing their applications’ potentiality on real world paradigms. We compare the performance obtained with (1) Multi-network RBF/LVQ structure (2) Discrete Hidden Markov Models (HMM) (3) Hybrid HMM/MLP system using a Multi LayerPerceptron (MLP) to estimate the HMM emission probabilitie...

Engineering field has inherently many combinatorial optimization problems which are hard to solve in some definite interval of time especially when input size is big. Although traditional algorithms yield most optimal answers, they need large amount of time to solve the problems. A new branch of algorithms known as evolutionary algorithms solve these problems in less time. Such algorithms have ...