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Density of nodes deployed in WSNs is based on application requirements. The redundant data collection in dense network results in more energy consumption. The Data Routing In-Network Aggregation (DRINA) is one of the recent algorithm proposed to reduce energy consumption in dense network environment by minimizing the number of communications from source to sink. Here the Data transmission is carried...
Spatial clustering is a very important tool in the analysis of spatial data. In this paper, we propose a novel density based spatial clustering algorithm called K-DBSCAN with the main focus of identifying clusters of points with similar spatial density. This contrasts with many other approaches, whose main focus is spatial contiguity. The strength of K-DBSCAN lies in finding arbitrary shaped clusters...
Clustering of sub-trajectories is a very useful method to extract important information from vast amounts of trajectory data. Existing trajectory clustering algorithms have focused on geometric properties and spatial features of trajectories and sub-trajectories. In contrast to the existing trajectory clustering algorithms, we propose a new framework to cluster sub-trajectories based on a combination...
Significant effort has been devoted to designing clustering algorithms that are responsive to user feedback or that incor- porate prior domain knowledge in the form of constraints. However, users desire more expressive forms of interaction to influence clustering outcomes. In our experiences working with diverse application scientists, we have identified an interaction style scat- ter/gather clustering...
Data clustering is one of the powerful techniques for the knowledge discovery from data. In this paper, a novel approach for hierarchical clustering has been proposed over non-binary search space. Besides the agglomerative methods, the proposed algorithm has considered the Strength of Presence associated with each transaction, to yield quality clusters which are again more close to the real life situation...
Association Rule Induction and Clustering are some of the most useful data mining techniques. The former focuses on finding regularities in data trends, while the latter on discovering groups and identifying interesting distributions and patterns in the underlying data. Several research attempts have been made for the purpose of clustering transactions based on the binary data space. In this paper,...
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