Results 241 to 250 of about 315,009 (291)

A Hierarchical Clustering Based Heuristic for Automatic Clustering

Proceedings of the 6th International Conference on Agents and Artificial Intelligence, 2014
Determining an optimal number of clusters and producing reliable results are two challenging and critical tasks in cluster analysis. We propose a clustering method which produces valid results while automatically determining an optimal number of clusters.
LaPlante, François, Belacel, Nabil, Kardouchi, Mustapha   +2 more
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DICLENS: Divisive Clustering Ensemble with Automatic Cluster Number

IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2012
Clustering has a long and rich history in a variety of scientific fields. Finding natural groupings of a data set is a hard task as attested by hundreds of clustering algorithms in the literature. Each clustering technique makes some assumptions about the underlying data set. If the assumptions hold, good clusterings can be expected.
Selim, Mimaroglu, Emin, Aksehirli
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Automatic UWB clusters identification

2009 IEEE Radio and Wireless Symposium, 2009
Clustering phenomenon exists in the Ultra-Wideband (UWB) impulse responses. Although it is feasible to manually identify clusters via visual inspection, this task becomes very difficult and time consuming when a large amount of data needs to be processed.
Michael Corrigan, Asia Walton, null Weihong Niu, null Jia Li, Timothy Talty   +4 more
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Automatic Bitcoin Address Clustering

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), 2017
Bitcoin is digital assets infrastructure powering the first worldwide decentralized cryptocurrency of the same name. All history of Bitcoins owning and transferring (addresses and transactions) is available as a public ledger called blockchain. But real-world owners of addresses are not known in general.
Dmitry Ermilov, Maxim Panov, Yury Yanovich   +2 more
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Automatic Cluster Detection in Kohonen's SOM

IEEE Transactions on Neural Networks, 2008
Kohonen's self-organizing map (SOM) is a popular neural network architecture for solving problems in the field of explorative data analysis, clustering, and data visualization. One of the major drawbacks of the SOM algorithm is the difficulty for nonexpert users to interpret the information contained in a trained SOM.
Dominik, Brugger, Martin, Bogdan, Wolfgang, Rosenstiel   +2 more
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Clustering Based Automatic Segmentation of Liver

2007 IEEE 15th Signal Processing and Communications Applications, 2007
Identifying the liver region, calculation of the liver volume and determination of the vessel structure from abdominal computed tomography datasets are some of the essential steps in visualization prior to the hepatic surgery. Because of the high number of slices, manual segmentation of the liver is time consuming, tedious and depends on the experience.
KOCAOĞLU, AYKUT, KALAYCI DEMİR, GÜLESER, Guezelis, Cueneyt, SELVER, MUSTAFA ALPER   +3 more
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Towards automatic clustering of protein sequences

Proceedings. IEEE Computer Society Bioinformatics Conference, 2003
Analyzing protein sequence data becomes increasingly important recently. Most previous work on this area has mainly focused on building classification models. In this paper, we investigate in the problem of automatic clustering of unlabeled protein sequences.
Jiong, Yang, Wei, Wang
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Symmetry-Based Automatic Clustering

2013
This chapter deals with some automatic clustering techniques, where the number of clusters need not be fixed a priori. First some recently developed genetic algorithm-based automatic clustering techniques are described briefly. Thereafter a recently developed point symmetry-based automatic genetic clustering technique, VGAPS, is described in detail; it
Sanghamitra Bandyopadhyay, Sriparna Saha
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Automatic Determination of Clusters

2007
In this paper we propose an automatic method for spectral clustering of weighted directed graphs. It is based on the eigensystem of a complex Hermitian adjacency matrix H n×n . The number of relevant clusters is determined automatically. Nodes are assigned to clusters using the inner product matrix S n×n calculated from a matrix R n×l of the l ...
Hoser, B., Schröder, J.
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Kernel-Based Clustering with Automatic Cluster Number Selection

2011 IEEE 11th International Conference on Data Mining Workshops, 2011
Kernel k-means is one of the most well-known kernel-based clustering methods for discovering nonlinearly separable clusters. However, like its original counterpart k-means, kernel k-means has two inherent drawbacks: (1) it is easily trapped into degenerate local minima when the prototypes of clusters are ill-initialized, and (2) the actual number of ...
Chang-Dong Wang, Jian-Huang Lai, Dong Huang   +2 more
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