Cluster analysis is the automated search for groups of related observations in a data set. Most clustering done in practice is based largely on heuristic but intuitively reasonable procedures and most clustering methods available in commercial software are also of this type. However

of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, we present the new clustering algorithm DBSCAN relying on a density-based notion of clusters which is designed to discover

the intrinsic clustering structure accurately. We introduce a new algorithm for the purpose of cluster analysis which does not produce a clustering of a data set explicitly; but instead creates an augmented ordering of the database representing its density-based clustering structure. This cluster

bounds on node density and intracluster and intercluster transmission ranges, HEED can asymptotically almost surely guarantee connectivity of clustered networks. Simulation results demonstrate that our proposed approach is effective in prolonging the network lifetime and supporting scalable data

of clustering, density modelling and local dimensionality reduction, and we demonstrate its applicat...

- whenever a new data set is received. ties of Joint hypotheses are calculated recursively using all available The a! onthm can easurements fro inforv~~Hnsuch as density of wiknown targets, density of false ~ g use m rn two ~.~erprobability of ietectlon, ami location ~rtainty. mis ~iciiing tecii- cut generic

Sensor networks are distributed event-based systems that differ from traditional communication networks in several ways: sensor networks have severe energy constraints, redundant low-rate data, and many-to-one flows. Datacentric mechanisms that perform in-network aggregation of data are needed

sensor networks. Based on this approach, we present a protocol, HEED (Hybrid Energy-Efficient Distributed clustering), that periodically selects cluster heads according to a hybrid of their residual energy and a secondary parameter, such as node proximity to its neighbors or node degree. HEED does

Abstract. Ants, bees and other social insects deposit pheromone (a type of chemical) in order to communicate between the members of their community. Pheromone that causes clumping or clustering behavior in a species and brings individuals into a closer proximity is called aggrega-tion pheromone

functions of the data points. Clusters can then be identified by determining density-attractors and clusters of arbitrary shape can be easily described by a simple equation based on the overall density function. The advantages of our new approach are (1) it has a firm mathematical basis, (2) it has good