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The Impact of Triangular Inequality Violations on Medoid-Based Clustering

by Saaid Baraty, Dan A. Simovici, Catalin Zara
"... We evaluate the extent to which a dissimilarity space differs from a metric space by introducing the notion of metric point and metricity in a dissimilarity space. The the effect of triangular inequality violations on medoid-based clustering of objects in a dissimilarity space is examined and the no ..."
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We evaluate the extent to which a dissimilarity space differs from a metric space by introducing the notion of metric point and metricity in a dissimilarity space. The the effect of triangular inequality violations on medoid-based clustering of objects in a dissimilarity space is examined

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 511 (49 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

ROCK: A Robust Clustering Algorithm for Categorical Attributes

by Sudipto Guha, Rajeev Rastogi, Kyuseok Shim - In Proc.ofthe15thInt.Conf.onDataEngineering , 2000
"... Clustering, in data mining, is useful to discover distribution patterns in the underlying data. Clustering algorithms usually employ a distance metric based (e.g., euclidean) similarity measure in order to partition the database such that data points in the same partition are more similar than point ..."
Abstract - Cited by 430 (2 self) - Add to MetaCart
Clustering, in data mining, is useful to discover distribution patterns in the underlying data. Clustering algorithms usually employ a distance metric based (e.g., euclidean) similarity measure in order to partition the database such that data points in the same partition are more similar than

Shape Matching and Object Recognition Using Shape Contexts

by Serge Belongie, Jitendra Malik, Jan Puzicha - IEEE Transactions on Pattern Analysis and Machine Intelligence , 2001
"... We present a novel approach to measuring similarity between shapes and exploit it for object recognition. In our framework, the measurement of similarity is preceded by (1) solv- ing for correspondences between points on the two shapes, (2) using the correspondences to estimate an aligning transform ..."
Abstract - Cited by 1787 (21 self) - Add to MetaCart
We present a novel approach to measuring similarity between shapes and exploit it for object recognition. In our framework, the measurement of similarity is preceded by (1) solv- ing for correspondences between points on the two shapes, (2) using the correspondences to estimate an aligning

The Elements of Statistical Learning -- Data Mining, Inference, and Prediction

by Trevor Hastie, Robert Tibshirani, Jerome Friedman
"... ..."
Abstract - Cited by 1320 (13 self) - Add to MetaCart
Abstract not found

Chebyshev and Fourier Spectral Methods

by John P. Boyd , 1999
"... ..."
Abstract - Cited by 778 (12 self) - Add to MetaCart
Abstract not found

Wireless Communications

by Andrea Goldsmith, Anaïs Nin , 2005
"... Copyright c ○ 2005 by Cambridge University Press. This material is in copyright. Subject to statutory exception and to the provisions of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University ..."
Abstract - Cited by 1129 (32 self) - Add to MetaCart
Copyright c ○ 2005 by Cambridge University Press. This material is in copyright. Subject to statutory exception and to the provisions of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University

Channel Assignment Schemes for Cellular Mobile Telecommunication Systems

by I. Katzela, M. Naghshineh - IEEE Personal Communications , 1996
"... This paper provides a detailed discussion of wireless resource and channel allocation schemes. We provide a survey of a large number of published papers in the area of fixed, dynamic and hybrid allocation schemes and compare their trade-offs in terms of complexity and performance. We also investigat ..."
Abstract - Cited by 386 (1 self) - Add to MetaCart
investigate these channel allocation schemes based on other factors such as distributed/centralized control and adaptability to traffic conditions. Moreover, we provide a detailed discussion on reuse partitioning schemes, effect of hand-offs and prioritization schemes. Finally, we discuss other important

On Clustering Validation Techniques

by Maria Halkidi, Yannis Batistakis, Michalis Vazirgiannis - Journal of Intelligent Information Systems , 2001
"... Cluster analysis aims at identifying groups of similar objects and, therefore helps to discover distribution of patterns and interesting correlations in large data sets. It has been subject of wide research since it arises in many application domains in engineering, business and social sciences. Esp ..."
Abstract - Cited by 283 (2 self) - Add to MetaCart
. Especially, in the last years the availability of huge transactional and experimental data sets and the arising requirements for data mining created needs for clustering algorithms that scale and can be applied in diverse domains.

CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling

by George Karypis , Eui-Hong (Sam) Han, Vipin Kumar , 1999
"... Clustering in data mining is a discovery process that groups a set of data such that the intracluster similarity is maximized and the intercluster similarity is minimized. Existing clustering algorithms, such as K-means, PAM, CLARANS, DBSCAN, CURE, and ROCK are designed to find clusters that fit s ..."
Abstract - Cited by 272 (23 self) - Add to MetaCart
consists of clusters that are of diverse shapes, densities, and sizes. In this paper, we present a novel hierarchical clustering algorithm called CHAMELEON that measures the similarity of two clusters based on a dynamic model. In the clustering process, two clusters are merged only if the inter
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