Entropy-based criterion in categorical clustering

Li, Tao; Ma, Sheng; Ogihara, Mitsunori

doi:10.1145/1015330.1015404

Cited by 133 publications

(135 citation statements)

References 14 publications

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“…Initial results have shown that the entropy criterion can be very effective in clustering categorical data. Paper [34] also shows that the entropy criterion can be formally derived in the framework of probabilistic clustering models, which strongly supports that the entropy criterion is a meaningful and reliable similarity measure, particularly good for categorical data.…”

Section: Entropy-based Categorical Clusteringmentioning

confidence: 53%

“…Originated from information theory, entropy has been applied in various areas, such as pattern discovery [8], numerical clustering [17] and information retrieval [40]. Due to the lack of intuitive distance definition for categorical values, recently entropy has been applied in clustering categorical data [5,34,11,20,13]. Initial results have shown that the entropy criterion can be very effective in clustering categorical data.…”

Section: Entropy-based Categorical Clusteringmentioning

confidence: 99%

“…Clustering structure at some time window In entropy-based categorical clustering, the quality of clustering is essentially evaluated by the entropy criterion, namely, the Expected Entropy of clusters [5,34]. Other variants, such as Minimum Description Length (MDL) [11] or mutual information [20,3], can also be unified under the probabilistic clustering framework [34].…”

Section: C2mentioning

confidence: 99%

“…The initial research has shown that clustering stream data can provide important clues about emerging data patterns, so that decision makers can predict new coming events and react promptly. As many data streams also include categorical data, discretizing the numerical part and unifying both types of data under the categorical data clustering framework [5,34,13] is one convenient method. Surprisingly, very few [5] have addressed the problems of clustering categorical data streams, and none addressed the problems of monitoring the change of clustering structure in categorical data streams.…”

Section: Introductionmentioning

confidence: 99%

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HE-Tree: a framework for detecting changes in clustering structure for categorical data streams

Chen

Liu

2009

The VLDB Journal

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Analyzing clustering structures in data streams can provide critical information for real-time decision making. Most research in this area has focused on clustering algorithms for numerical data streams, and very few have proposed to monitor the change of clustering structure. Most surprisingly, to our knowledge, no work has been proposed on monitoring clustering structure for categorical data streams. In this paper, we present a framework for detecting the change of primary clustering structure in categorical data streams, which is indicated by the change of the best number of clusters (Best K) in the data stream. The framework uses a Hierarchical Entropy Tree structure (HE-Tree) to capture the entropy characteristics of clusters in a data stream, and detects the change of Best K by combining our previously developed BKPlot method. The HE-Tree can efficiently summarize the entropy property of a categorical data stream and allow us to draw precise clustering information from the data stream for generating high-quality BKPlots. We also develop the time-decaying HE-Tree structure to make the monitoring more sensitive to recent changes of clustering structure. The experimental result shows that with the combination of the HE-Tree and the BKPlot method we are able to promptly and precisely detect the change of clustering structure in categorical data streams.

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Section: Entropy-based Categorical Clusteringmentioning

confidence: 53%

Section: Entropy-based Categorical Clusteringmentioning

confidence: 99%

Section: C2mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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HE-Tree: a framework for detecting changes in clustering structure for categorical data streams

Chen

Liu

2009

The VLDB Journal

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“…In fact, entropy and related information gain criterion are widely used in learning, e.g. for contextual clustering [25] or decision trees [34].…”

Section: Highmentioning

confidence: 99%

Pseudo-bound Optimization for Binary Energies

Tang

Ayed

Boykov

2014

Computer Vision – ECCV 2014

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Abstract. High-order and non-submodular pairwise energies are important for image segmentation, surface matching, deconvolution, tracking and other computer vision problems. Minimization of such energies is generally NP-hard. One standard approximation approach is to optimize an auxiliary function -an upper bound of the original energy across the entire solution space. This bound must be amenable to fast global solvers. Ideally, it should also closely approximate the original functional, but it is very difficult to find such upper bounds in practice.Our main idea is to relax the upper-bound condition for an auxiliary function and to replace it with a family of pseudo-bounds, which can better approximate the original energy. We use fast polynomial parametric maxflow approach to explore all global minima for our family of submodular pseudo-bounds. The best solution is guaranteed to decrease the original energy because the family includes at least one auxiliary function. Our Pseudo-Bound Cuts algorithm improves the state-of-the-art in many applications: appearance entropy minimization, target distribution matching, curvature regularization, image deconvolution and interactive segmentation.

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D irichlet Distribution

Lin¹

2004

Encyclopedic Dictionary of Genetics, Genomics and Proteomics

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Entropy-based criterion in categorical clustering

Cited by 133 publications

References 14 publications

HE-Tree: a framework for detecting changes in clustering structure for categorical data streams

HE-Tree: a framework for detecting changes in clustering structure for categorical data streams

Pseudo-bound Optimization for Binary Energies

D irichlet Distribution

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