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Model-Based Clustering, Discriminant Analysis, and Density Estimation

by Chris Fraley, Adrian E. Raftery - JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION , 2000
"... 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, there is little ..."
Abstract - Cited by 573 (29 self) - Add to MetaCart
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

A density-based algorithm for discovering clusters in large spatial databases with noise

by Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu , 1996
"... Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clu ..."
Abstract - Cited by 1786 (70 self) - Add to MetaCart
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

OPTICS: Ordering Points To Identify the Clustering Structure

by Mihael Ankerst, Markus M. Breunig, Hans-peter Kriegel, Jörg Sander , 1999
"... Cluster analysis is a primary method for database mining. It is either used as a stand-alone tool to get insight into the distribution of a data set, e.g. to focus further analysis and data processing, or as a preprocessing step for other algorithms operating on the detected clusters. Almost all of ..."
Abstract - Cited by 527 (51 self) - Add to MetaCart
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

HEED: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad Hoc Sensor Networks

by Ossama Younis, Sonia Fahmy - IEEE TRANS. MOBILE COMPUTING , 2004
"... Topology control in a sensor network balances load on sensor nodes and increases network scalability and lifetime. Clustering sensor nodes is an effective topology control approach. In this paper, we propose a novel distributed clustering approach for long-lived ad hoc sensor networks. Our proposed ..."
Abstract - Cited by 590 (1 self) - Add to MetaCart
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

Mixtures of Probabilistic Principal Component Analysers

by Michael E. Tipping, Christopher M. Bishop , 1998
"... Principal component analysis (PCA) is one of the most popular techniques for processing, compressing and visualising data, although its effectiveness is limited by its global linearity. While nonlinear variants of PCA have been proposed, an alternative paradigm is to capture data complexity by a com ..."
Abstract - Cited by 532 (6 self) - Add to MetaCart
of clustering, density modelling and local dimensionality reduction, and we demonstrate its applicat...

An Algorithm for Tracking Multiple Targets

by Donald B. Reid - IEEE Transactions on Automatic Control , 1979
"... Abstract—An algorithm for tracking multiple targets In a cluttered algorithms. Clustering is the process of dividing the entire environment Is developed. The algorithm Is capable of Initiating tracks, set of targets and measurements into independent groups accounting for false or m[~clngreports, and ..."
Abstract - Cited by 596 (0 self) - Add to MetaCart
- 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

The Impact of Data Aggregation in Wireless Sensor Networks

by Bhaskar Krishnamachari, Deborah Estrin, Stephen Wicker
"... 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 in th ..."
Abstract - Cited by 400 (12 self) - Add to MetaCart
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

Distributed Clustering in Ad-hoc Sensor Networks: A Hybrid, Energy-Efficient Approach

by Ossama Younis, Sonia Fahmy , 2004
"... Prolonged network lifetime, scalability, and load balancing are important requirements for many ad-hoc sensor network applications. Clustering sensor nodes is an effective technique for achieving these goals. In this work, we propose a new energy-efficient approach for clustering nodes in adhoc sens ..."
Abstract - Cited by 307 (12 self) - Add to MetaCart
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

Aggregation Pheromone Density Based Image Segmentation

by Susmita Ghosh, Megha Kothari, Ashish Ghosh
"... 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. Thi ..."
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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

An Efficient Approach to Clustering in Large Multimedia Databases with Noise".

by Alexander Hinneburg , Daniel A Keim - Proceeding of KDD '98. , 1998
"... Abstract Several clustering algorithms can be applied to clustering in large multimedia databases. The effectiveness and efficiency of the existing algorithms, however, is somewhat limited, since clustering in multimedia databases requires clustering high-dimensional feature vectors and since multi ..."
Abstract - Cited by 278 (13 self) - Add to MetaCart
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
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