There is often a need to cluster voluminous amounts of data. Such clustering has application in fields such as pattern recognition, data mining, bioinformatics, and recommendation systems. Here we evaluate the perform...
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An upgraded ANN model and clustering algorithm are coupled in a suggested combined model forecasting technique to increase the prediction accuracy of power system load forecasting. The samples are clustered using the ...
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clustering can be defined as a problem of partitioning a given data into non-hierarchical groups of items. In our previous work, we suggested an information-theoretic criterion for defining the goodness of a clusterin...
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ISBN:
(纸本)1601320795
clustering can be defined as a problem of partitioning a given data into non-hierarchical groups of items. In our previous work, we suggested an information-theoretic criterion for defining the goodness of a clustering of data. The basic idea behind this framework is to optimize the total code length over the data by encoding together data items belonging to the same cluster. Formally the global code length criterion to be optimized is defined by using the theoretically and intuitively appealing universal normalized maximum likelihood (NML) code. In this paper, we focus on the optimization aspect of the clustering problem, and study five algorithms that can be used for efficiently searching the exponentially-sized clustering space. The number of clusters is not known beforehand and determining it is part of the optimization process. In the empirical part of the paper we compare the performance of the suggested algorithms using several real-world datasets.
To complete the on-board equipment of space systems with a highly reliable electronic component base (ECB), specialized test centers perform hundreds of tests to analyze each semiconductor device. One of the requireme...
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In recent years, local graph clustering techniques have been utilized as devices to unveil the structured hidden of large networks. With the ever growing size of the data sets generated in domains of applications as d...
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Based on the probability model of clustering algorithm constructs a model for each cluster, calculate probability of every text falls in different models to decide text belongs to which cluster, conveniently in global...
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ISBN:
(纸本)9783037852903
Based on the probability model of clustering algorithm constructs a model for each cluster, calculate probability of every text falls in different models to decide text belongs to which cluster, conveniently in global Angle represents abstract structure of clusters. In this paper combining the hidden Markov model and k - means clustering algorithm realize text clustering, first produces first clustering results by k - means algorithm, as the initial probability model of a hidden Markov model, constructed probability transfer matrix prediction every step of clustering iteration, when subtraction value of two probability transfer matrix is 0, clustering end. This algorithm can in global perspective every cluster of document clustering process, to avoid the repetition of clustering process, effectively improve the clustering algorithm.
Despite the complete sequencing of human genome, most of the gene functions are still unknown. Micro array techniques provides a fast and reliable means to analysis of the gene expression and the understanding of thei...
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clustering is an unsupervised learning technique used to group a set of elements into nonoverlapping clusters based on some predefined dissimilarity function. In our context, we rely on clustering algorithms to extrac...
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clustering is an unsupervised learning technique used to group a set of elements into nonoverlapping clusters based on some predefined dissimilarity function. In our context, we rely on clustering algorithms to extract points of interest in human mobility as an inference attack for quantifying the impact of the privacy breach. Thus, we focus on the input parameters selection for the clustering algorithm, which is not a trivial task due to the direct impact of these parameters in the result of the attack. Namely, if we use too relax parameters we will have too many point of interest but if we use a too restrictive set of parameters, we will find too few groups. Accordingly, to solve this problem, we propose a method to select the best parameters to extract the optimal number of POIs based on quality metrics.
In this paper, from the perspective of creating and maintaining geological or geoecological models, methodological and technical issues, ways of developing the system GeoBazaDannych (GBD), expanding its functionality ...
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We have given a comprehensive comparative analysis of various clustering algorithms. clustering algorithms usually employ distance metric or similarity matrix to cluster the data set into different partitions. Well kn...
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