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Demystifying Information-Theoretic Clustering
- Proceedings of The 31st International Conference on Machine Learning; arXiv:1310.4210
, 2014
"... We propose a novel method for clustering data which is grounded in information-theoretic prin-ciples and requires no parametric assumptions. Previous attempts to use information theory to define clusters in an assumption-free way are based on maximizing mutual information be-tween data and cluster l ..."
Abstract
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Cited by 3 (1 self)
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We propose a novel method for clustering data which is grounded in information-theoretic prin-ciples and requires no parametric assumptions. Previous attempts to use information theory to define clusters in an assumption-free way are based on maximizing mutual information be-tween data and cluster
C.: Robust information-theoretic clustering
- In: KDD
, 2006
"... How do we find a natural clustering of a real world point set, which contains an unknown number of clusters with different shapes, and which may be contaminated by noise? Most clustering algorithms were designed with certain as-sumptions (Gaussianity), they often require the user to give input param ..."
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Cited by 10 (4 self)
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parameters, and they are sensitive to noise. In this pa-per, we propose a robust framework for determining a nat-ural clustering of a given data set, based on the minimum description length (MDL) principle. The proposed frame-work, Robust Information-theoretic Clustering (RIC), is or-thogonal to any known
ABSTRACT Robust Information-theoretic Clustering
"... How do we find a natural clustering of a real world point set, which contains an unknown number of clusters with different shapes, and which may be contaminated by noise? Most clustering algorithms were designed with certain assumptions (Gaussianity), they often require the user to give input parame ..."
Abstract
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parameters, and they are sensitive to noise. In this paper, we propose a robust framework for determining a natural clustering of a given data set, based on the minimum description length (MDL) principle. The proposed framework, Robust Information-theoretic Clustering (RIC), is orthogonal to any known
ITCH: information-theoretic cluster hierarchies
- In Proceedings of the European conference on machine learning and knowledge discovery in databases (ECML PKDD
, 2010
"... Abstract. Hierarchical clustering methods are widely used in various scientific domains such as molecular biology, medicine, economy, etc. Despite the maturity of the research field of hierarchical clustering, we have identified the following four goals which are not yet fully satisfied by previous ..."
Abstract
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Cited by 2 (0 self)
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parameter settings. With ITCH, we propose a novel clus-tering method that is built on a hierarchical variant of the information-theoretic principle of Minimum Description Length (MDL), referred to as hMDL. Inter-preting the hierarchical cluster structure as a statistical model of the data set, it can
Information Theoretic Clustering using Minimum Spanning Trees
"... Abstract. In this work we propose a new information-theoretic clustering algorithm that infers cluster memberships by direct optimization of a non-parametric mutual information estimate between data distribution and cluster assignment. Although the optimization objective has a solid theoretical foun ..."
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Cited by 5 (0 self)
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Abstract. In this work we propose a new information-theoretic clustering algorithm that infers cluster memberships by direct optimization of a non-parametric mutual information estimate between data distribution and cluster assignment. Although the optimization objective has a solid theoretical
Information Theoretical Clustering via Semidefinite Programming
"... We propose techniques of convex optimization for information theoretical clustering. The clustering objective is to maximize the mutual information between data points and cluster assignments. We formulate this problem first as an instance of max k cut on weighted graphs. We then apply the technique ..."
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Cited by 1 (1 self)
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We propose techniques of convex optimization for information theoretical clustering. The clustering objective is to maximize the mutual information between data points and cluster assignments. We formulate this problem first as an instance of max k cut on weighted graphs. We then apply
Efficient Information Theoretic Clustering on Discrete Lattices
"... We consider the problem of clustering data that reside on discrete, low dimensional lattices. Canonical examples for this setting are found in image segmentation and key point extraction. Our solution is based on a recent approach to information theoretic clustering where clusters result from an ite ..."
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Cited by 1 (1 self)
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We consider the problem of clustering data that reside on discrete, low dimensional lattices. Canonical examples for this setting are found in image segmentation and key point extraction. Our solution is based on a recent approach to information theoretic clustering where clusters result from
Information Theoretic Clustering of Sparse Co-Occurrence Data
"... A novel approach to clustering co-occurrence data poses it as an optimization problem in information theory which minimizes the resulting loss in mutual information. A divisive clustering algorithm that monotonically reduces this loss function was recently proposed. In this paper we show that sparse ..."
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local minima. Finally, we combine these solutions to get a robust algorithm that is computationally efficient. We present experimental results to show that the proposed method is effective in clustering document collections and outperforms previous information-theoretic clustering approaches. 1
Sail: summation-based incremental learning for information-theoretic clustering
- In KDD
, 2008
"... Information-theoretic clustering aims to exploit information theoretic measures as the clustering criteria. A common practice on this topic is so-called INFO-K-means, which performs K-means clustering with the KL-divergence as the proximity function. While expert efforts on INFO-K-means have shown p ..."
Abstract
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Cited by 4 (0 self)
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Information-theoretic clustering aims to exploit information theoretic measures as the clustering criteria. A common practice on this topic is so-called INFO-K-means, which performs K-means clustering with the KL-divergence as the proximity function. While expert efforts on INFO-K-means have shown
Information theoretic clustering of sparse co-occurrence data
- In Proceedings of the Third IEEE International Conference on Data Mining (ICDM-03
, 2003
"... ..."
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