—Wireless sensor networks (WSNs) suffers from the hot spot problem where the sensor nodes closest to the base station are need to relay more packet than the nodes farther away from the base station. Thus, lifetime of sensory network depends on these closest nodes. Clustering methods are used to extend the lifetime of a wireless sensor network. However,… (More)
All clustering methods have to assume some cluster relationship among the data objects that they are applied on. Hierarchical clustering builds(agglomerative),or breaks up(divisive), a hierarchy of clusters. The traditional representation of this hierarchy is a tree. In this paper, we introduce to develop a novel hierarchal algorithm for document clustering… (More)
Wireless sensor networks (WSNs) suffers from the hot spot problem where the sensor nodes closest to the base station are need to relay more packet than the nodes farther away from the base station. Thus, lifetime of sensory network depends on these closest nodes. Clustering methods are used to extend the lifetime of a wireless sensor network. However,… (More)
In this paper we have investigated the performance of PSO Particle Swarm Optimization based clustering on few real world data sets and one artificial data set. The performances are measured by two metric namely quantization error and inter-cluster distance. The K means clustering algorithm is first implemented for all data sets, the results of which form… (More)
In this paper we propose a simple scalable genetic programming multi-class ensemble classifier of higher accuracy. A formula is derived to obtain the maximum number of nodes permitted in a GP classifier. A wrapper approach for feature selection mechanism based on GP classifier is adopted in our work.
Text Mining is important, emerging, research area, because plenty of text resources growing rapidly through the internet and digital world. In the text data analysis text categorization is one of the vital techniques. Traditional text categorization methods are not able to handle well with learning across different domains. Cross-domain classification is… (More)
In this paper, we present a novel density based trajectory clustering technique for clustering and visualizing Spatio-temporal data to analyze the navigational behavior of moving entities, such as users, virtual characters or vehicles. For testing our proposal, we developed DenTrac (Density based Trajectory Clustering and visualization tool for… (More)
Expectation Maximization (EM) is an efficient mixture-model based clustering method. In this paper, authors made an attempt to scale-up the algorithm, by reducing the computation time required for computing quadratic term, without sacrificing the accuracy. Probability density function (pdf) is to be calculated in EM, which involves evaluating quadratic term… (More)