The Experts below are selected from a list of 13704 Experts worldwide ranked by ideXlab platform
Anil Kumar Gupta - One of the best experts on this subject based on the ideXlab platform.
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A Comparative study Between Fuzzy Clustering Algorithm and Hard Clustering Algorithm
International Journal of Computer Trends and Technology, 2014Co-Authors: Dibya Jyoti Bora, Anil Kumar GuptaAbstract:Data Clustering is an important area of data mining. This is an unsupervised study where data of similar types are put into one cluster while data of another types are put into different cluster. Fuzzy C means is a very important Clustering technique based on fuzzy logic. Also we have some Hard Clustering techniques available like K-means among the popular ones. In this paper a comparative study is done between Fuzzy Clustering algorithm and Hard Clustering algorithm.
Pingxin Wang - One of the best experts on this subject based on the ideXlab platform.
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ce3 a three way Clustering method based on mathematical morphology
Knowledge Based Systems, 2018Co-Authors: Pingxin Wang, Yiyu YaoAbstract:Abstract Many existing Clustering methods produce clusters with clear and sharp boundaries, which does not truly reflect the fact that a cluster may not necessarily have a well-defined boundary in many real world situations. In this paper, by combining ideas of erosion and dilation from mathematical morphology and principles of three-way decision, we propose a framework of a contraction-and-expansion based three-way Clustering called CE3. A three-way cluster is defined by a nested pair of sets called the core and the support of the cluster, respectively. A stronger relationship holds between objects in the core and a weaker relationship holds between objects in the support. Given a cluster obtained from a Hard Clustering method, CE3 uses a contraction operation to shrink the cluster into the core of a three-way cluster and uses an expansion operation to enlarge the cluster into the support. The difference between the support and the core is called the fringe region, representing an unsharp boundary of a cluster. Within the CE3 framework, we can define different types of contraction and expansion operations. We can apply the CE3 framework on the top of any existing Clustering method. As examples for demonstration, we introduce a pair of neighbor-based contraction and expansion operations and apply the CE3 framework on the top of k-means and spectral Clustering, respectively. We use one synthetic data set, five UCI data sets, and three USPS data sets to evaluate experimentally the performance of CE3. The results show that CE3 is in fact effective in revealing cluster structures.
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ensemble re Clustering refinement of Hard Clustering by three way strategy
International Conference on Intelligent Science and Big Data Engineering, 2017Co-Authors: Pingxin Wang, Qiang Liu, Xibei YangAbstract:In this paper, we propose a three-way ensemble re-Clustering method based on ideas of cluster ensemble and three-way decision. In the proposed method, we use Hard Clustering methods to produce different Clustering results and cluster labels matching to align each Clustering results to a given order. The intersection of the clusters with same labels are regarded as the core region and the difference between the union and the intersection of the clusters with same labels are regarded as the fringe region of the specific cluster. Therefore, a three-way result of the cluster is naturally formed. The results on UCI data sets show that such strategy is effective in improving the structure of Clustering results and F\(_1\) values.
Dibya Jyoti Bora - One of the best experts on this subject based on the ideXlab platform.
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A Comparative study Between Fuzzy Clustering Algorithm and Hard Clustering Algorithm
International Journal of Computer Trends and Technology, 2014Co-Authors: Dibya Jyoti Bora, Anil Kumar GuptaAbstract:Data Clustering is an important area of data mining. This is an unsupervised study where data of similar types are put into one cluster while data of another types are put into different cluster. Fuzzy C means is a very important Clustering technique based on fuzzy logic. Also we have some Hard Clustering techniques available like K-means among the popular ones. In this paper a comparative study is done between Fuzzy Clustering algorithm and Hard Clustering algorithm.
Malle Julien - One of the best experts on this subject based on the ideXlab platform.
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Fuzzy Clustering: eine Anwendung auf distributionelles Reinforcement Learning
Wien, 2021Co-Authors: Malle JulienAbstract:Die meisten Reinforcement Learning-Algorithmen werden mit der Einschränkung eines diskreten (endlichen) Zustandsraums untersucht. Kompliziertere Zustandsräume werden normalerweise durch Funktionsnäherung behandelt, für die nur wenige theoretische Ergebnisse verfügbar sind. In dieser Arbeit wird eine clusterbasierte Näherung für kontinuierliche Zustandsräume untersucht. Die stückweise konstante Näherung, die durch (klassisches) hartes Clustering erhalten wird, wird empirisch unter Verwendung von Fuzzy-Menge und Zugehörigkeitsfunktionen verbessert. Wir untersuchen auch, wie das Clustering selbst mithilfe von Zugehörigkeitsfunktionen automatisiert werden kann, die auf das bekannte MNIST-Problem angewendet werden.Most Reinforcement Learning algorithms are studied with the restriction of a discrete (finite) state space. More complicated state spaces are usually handled through function approximation, for which few theoretical results are available. In this paper, a Clustering-based approximation for continuous state spaces is studied. The piecewise constant approximation obtained by (classical) Hard Clustering is enhanced empirically using fuzzy membership functions. We also look at how the Clustering itself could be automated, using membership functions applied to the well-known MNIST digit-recognition problem.4
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Fuzzy Clustering: eine Anwendung auf distributionelles Reinforcement Learning
Wien, 2021Co-Authors: Malle JulienAbstract:Arbeit an der Bibliothek noch nicht eingelangt - Daten nicht geprüftAbweichender Titel nach Übersetzung der Verfasserin/des VerfassersDie meisten Reinforcement Learning-Algorithmen werden mit der Einschränkung eines diskreten (endlichen) Zustandsraums untersucht. Kompliziertere Zustandsräume werden normalerweise durch Funktionsnäherung behandelt, für die nur wenige theoretische Ergebnisse verfügbar sind. In dieser Arbeit wird eine clusterbasierte Näherung für kontinuierliche Zustandsräume untersucht. Die stückweise konstante Näherung, die durch (klassisches) hartes Clustering erhalten wird, wird empirisch unter Verwendung von Fuzzy-Menge und Zugehörigkeitsfunktionen verbessert. Wir untersuchen auch, wie das Clustering selbst mithilfe von Zugehörigkeitsfunktionen automatisiert werden kann, die auf das bekannte MNIST-Problem angewendet werden.Most Reinforcement Learning algorithms are studied with the restriction of a discrete (finite) state space. More complicated state spaces are usually handled through function approximation, for which few theoretical results are available. In this paper, a Clustering-based approximation for continuous state spaces is studied. The piecewise constant approximation obtained by (classical) Hard Clustering is enhanced empirically using fuzzy membership functions. We also look at how the Clustering itself could be automated, using membership functions applied to the well-known MNIST digit-recognition problem.5
Michael I Jordan - One of the best experts on this subject based on the ideXlab platform.
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small variance asymptotics for exponential family dirichlet process mixture models
Neural Information Processing Systems, 2012Co-Authors: Ke Jiang, Brian Kulis, Michael I JordanAbstract:Sampling and variational inference techniques are two standard methods for inference in probabilistic models, but for many problems, neither approach scales effectively to large-scale data. An alternative is to relax the probabilistic model into a non-probabilistic formulation which has a scalable associated algorithm. This can often be fulfilled by performing small-variance asymptotics, i.e., letting the variance of particular distributions in the model go to zero. For instance, in the context of Clustering, such an approach yields connections between the k-means and EM algorithms. In this paper, we explore small-variance asymptotics for exponential family Dirichlet process (DP) and hierarchical Dirichlet process (HDP) mixture models. Utilizing connections between exponential family distributions and Bregman divergences, we derive novel Clustering algorithms from the asymptotic limit of the DP and HDP mixtures that features the scalability of existing Hard Clustering methods as well as the flexibility of Bayesian nonparametric models. We focus on special cases of our analysis for discrete-data problems, including topic modeling, and we demonstrate the utility of our results by applying variants of our algorithms to problems arising in vision and document analysis.
