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Finding Metric Structure in Information Theoretic Clustering
"... We study the problem of clustering discrete probability distributions with respect to the KullbackLeibler (KL) divergence. This problem arises naturally in many applications. Our goal is to pick k distributions as “representatives” such that the average or maximum KLdivergence between an input dist ..."
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Cited by 9 (0 self)
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We study the problem of clustering discrete probability distributions with respect to the KullbackLeibler (KL) divergence. This problem arises naturally in many applications. Our goal is to pick k distributions as “representatives” such that the average or maximum KLdivergence between an input
Density and Neighbor Adaptive Information Theoretic Clustering
"... Abstract—This work presents a novel clustering algorithm, ..."
Sail: summationbased incremental learning for informationtheoretic clustering
 In KDD
, 2008
"... Informationtheoretic clustering aims to exploit information theoretic measures as the clustering criteria. A common practice on this topic is socalled INFOKmeans, which performs Kmeans clustering with the KLdivergence as the proximity function. While expert efforts on INFOKmeans have shown p ..."
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Cited by 4 (0 self)
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Informationtheoretic clustering aims to exploit information theoretic measures as the clustering criteria. A common practice on this topic is socalled INFOKmeans, which performs Kmeans clustering with the KLdivergence as the proximity function. While expert efforts on INFOKmeans have shown
Generalized Information Theoretic Cluster Validity Indices for Soft Clusterings
"... There have been a large number of external validity indices proposed for cluster validity. One such class of cluster comparison indices is the information theoretic measures, due to their strong mathematical foundation and their ability to detect nonlinear relationships. However, they are devised ..."
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There have been a large number of external validity indices proposed for cluster validity. One such class of cluster comparison indices is the information theoretic measures, due to their strong mathematical foundation and their ability to detect nonlinear relationships. However, they are devised
FEATURE D ATA M INING TEXT MINING WITH INFORMATION–THEORETIC CLUSTERING
"... Motivated by the success of hybrid informationretrieval algorithms, the authors report on the development of their hybrid clustering scheme. Scheme experiments on data in a reduced vector space model indicate a higher performance level over several existing clustering algorithms. 15219615/03/$17.0 ..."
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Motivated by the success of hybrid informationretrieval algorithms, the authors report on the development of their hybrid clustering scheme. Scheme experiments on data in a reduced vector space model indicate a higher performance level over several existing clustering algorithms. 1521
Clustering with Bregman Divergences
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2005
"... A wide variety of distortion functions are used for clustering, e.g., squared Euclidean distance, Mahalanobis distance and relative entropy. In this paper, we propose and analyze parametric hard and soft clustering algorithms based on a large class of distortion functions known as Bregman divergence ..."
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Cited by 441 (59 self)
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divergences. The proposed algorithms unify centroidbased parametric clustering approaches, such as classical kmeans and informationtheoretic clustering, which arise by special choices of the Bregman divergence. The algorithms maintain the simplicity and scalability of the classical kmeans algorithm, while
An InformationTheoretic Definition of Similarity
 In Proceedings of the 15th International Conference on Machine Learning
, 1998
"... Similarity is an important and widely used concept. Previous definitions of similarity are tied to a particular application or a form of knowledge representation. We present an informationtheoretic definition of similarity that is applicable as long as there is a probabilistic model. We demonstrate ..."
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Cited by 1211 (0 self)
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Similarity is an important and widely used concept. Previous definitions of similarity are tied to a particular application or a form of knowledge representation. We present an informationtheoretic definition of similarity that is applicable as long as there is a probabilistic model. We demonstrate how our definition can be used to measure the similarity in a number of different domains.
InformationTheoretic CoClustering
 In KDD
, 2003
"... Twodimensional contingency or cooccurrence tables arise frequently in important applications such as text, weblog and marketbasket data analysis. A basic problem in contingency table analysis is coclustering: simultaneous clustering of the rows and columns. A novel theoretical formulation views ..."
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Cited by 342 (12 self)
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Twodimensional contingency or cooccurrence tables arise frequently in important applications such as text, weblog and marketbasket data analysis. A basic problem in contingency table analysis is coclustering: simultaneous clustering of the rows and columns. A novel theoretical formulation
Assessing coping strategies: A theoretically based approach
 Journal of Personality and Social Psychology
, 1989
"... We developed a multidimensional coping inventory to assess the different ways in which people respond to stress. Five scales (of four items each) measure conceptually distinct aspects of problemfocused coping (active coping, planning, suppression of competing activities, restraint coping, seeking ..."
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Cited by 610 (5 self)
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of emotions, behavioral disengagement, mental disengagement). Study 1 reports the development of scale items. Study 2 reports correlations between the various coping scales and several theoretically relevant personality measures in an effort to provide preliminary information about the inventory
Distance Metric Learning, With Application To Clustering With SideInformation
 ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 15
, 2003
"... Many algorithms rely critically on being given a good metric over their inputs. For instance, data can often be clustered in many "plausible" ways, and if a clustering algorithm such as Kmeans initially fails to find one that is meaningful to a user, the only recourse may be for the us ..."
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Cited by 799 (14 self)
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Many algorithms rely critically on being given a good metric over their inputs. For instance, data can often be clustered in many "plausible" ways, and if a clustering algorithm such as Kmeans initially fails to find one that is meaningful to a user, the only recourse may
Results 11  20
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2,381,076