Results 211 to 220 of about 2,690,463 (245)
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Clustering Criteria and Algorithms
2009Cluster analysis is an unsupervised pattern recognition frequently used in biology, where large amounts of data must often be classified. Hierarchical agglomerative approaches, the most commonly used techniques in biology, are described in this chapter.
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1991
Magnetic Resonance Imaging (MRI) plays a relevant role in the design of systems for computer assisted diagnosis. MR-images are multi-dimensional in nature; physicians have to combine several perceptual information images to perform the tissue classification needed for diagnosis. Automatic clustering methods help to discriminate relevant features and to
Vito Di Gesù +3 more
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Magnetic Resonance Imaging (MRI) plays a relevant role in the design of systems for computer assisted diagnosis. MR-images are multi-dimensional in nature; physicians have to combine several perceptual information images to perform the tissue classification needed for diagnosis. Automatic clustering methods help to discriminate relevant features and to
Vito Di Gesù +3 more
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The GridOPTICS clustering algorithm [PDF]
Intelligent Data Analysis, 2016 The OPTICS algorithm is a hierarchical density-based clustering method. It creates reachability plots to identify all clusters in the point set. Nevertheless, it has limitation, namely it is very slow for large data sets. We introduce the GridOPTICS algorithm, which builds a grid structure to reduce the number of data points, then it applies the OPTICS
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Adaptive Clustering Algorithms
2006This paper proposes an adaptive clustering approach. We focus on re-clustering an object set, previously clustered, when the feature set characterizing the objects increases. We have developed adaptive extensions for two traditional clustering algorithms (k-means and Hierarchical Agglomerative Clustering).
Alina Campan, Gabriela Serban
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A Practical Clustering Algorithm
2008We present a novel clustering algorithm (SDSA algorithm) based on the concept of the short distance of the consecutive points and the small angle between the consecutive vectors formed by three adjacent points. Not only the proposed SDSA algorithm is suitable for almost all test data sets used by Chung and Liu for point symmetry-based K-means algorithm
Wei Li 0158, Haohao Li, Jianye Chen
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An Evolutionary Clustering Algorithm
2004There are many heuristic algorithms for clustering, from which the most important are the hierarchical methods of agglomeration, especially the Ward’s method. Among the iterative methods the most universally used is the C–means method and it’s generalizations.
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Range clustering: An algorithm for empirical evaluation of classical clustering algorithms
2016 Ninth International Conference on Contemporary Computing (IC3), 2016Cluster analysis is a principal method in analytics domain of data mining. The algorithm used for clustering directly influences the results obtained from applying the clustering algorithm (clusters). Data clustering is done in order to identify the patterns and trends not identifiable from just looking at the data. Clustering may be supervised (if the
Nishant Arora +2 more
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An automated clustering algorithm based on agglomerative clustering
2016 24th Signal Processing and Communication Application Conference (SIU), 2016The most important one of main problems for K-based clustering algorithm is randomly selected k parameter when running the algorithm. In this study, an automated clustering algorithm based on agglomerative clustering and clusters without taking k parameter from user have been proposed.
Armagan Karabina, Erdal Kiliç
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How to “alternatize” a clustering algorithm
Data Mining and Knowledge Discovery, 2012zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mahmud Shahriar Hossain +3 more
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Genetic algorithms for clustering and fuzzy clustering
WIREs Data Mining and Knowledge Discovery, 2011AbstractClustering has been an area of intensive research for several decades because of its multifaceted applications in innumerable domains. Clustering can be either Boolean, where a single data point belongs to exactly one cluster, or fuzzy, where a single data point can have nonzero belongingness to more than one cluster.
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