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Results 1 to 25 of 805

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Definition of agroclimatic regions in Ireland using hydro-thermal and crop yield dataHOLDEN, N. M; BRERETON, A. J.Agricultural and forest meteorology. 2004, Vol 122, Num 3-4, pp 175-191, issn 0168-1923, 17 p.Article

Plasticity in the echolocation inventory of Mormopterus minutus (Chiroptera, Molossidae)MORA, Emanuel C; IBANEZ, Carlos; MACIAS, Silvio et al.Acta chiropterologica. 2011, Vol 13, Num 1, pp 179-187, issn 1508-1109, 9 p.Article

Cluster analysis of BOLD fMRI time series in tumors to study the heterogeneity of hemodynamic response to treatmentBAUDELET, Christine; GALLEZ, Bernard.Magnetic resonance in medicine. 2003, Vol 49, Num 6, pp 985-990, issn 0740-3194, 6 p.Article

Hierarchical initialization approach for K-Means clusteringLU, J. F; TANG, J. B; TANG, Z. M et al.Pattern recognition letters. 2008, Vol 29, Num 6, pp 787-795, issn 0167-8655, 9 p.Article

A three-phase approach to document clustering based on topic significance degreeYINGLONG MA; YAO WANG; BEIHONG JIN et al.Expert systems with applications. 2014, Vol 41, Num 18, pp 8203-8210, issn 0957-4174, 8 p.Article

The MinMax k-Means clustering algorithmTZORTZIS, Grigorios; LIKAS, Aristidis.Pattern recognition. 2014, Vol 47, Num 7, pp 2505-2516, issn 0031-3203, 12 p.Article

Fast modified global k-means algorithm for incremental cluster constructionBAGIROV, Adil M; UGON, Julien; WEBB, Dean et al.Pattern recognition. 2011, Vol 44, Num 4, pp 866-876, issn 0031-3203, 11 p.Article

Modified global k-means algorithm for minimum sum-of-squares clustering problemsBAGIROV, Adil M.Pattern recognition. 2008, Vol 41, Num 10, pp 3192-3199, issn 0031-3203, 8 p.Article

Analysis of global k-means, an incremental heuristic for minimum sum-of-squares clusteringHANSEN, Pierre; NGAI, Eric; CHEUNG, Bernard K et al.Journal of classification. 2005, Vol 22, Num 2, pp 287-310, issn 0176-4268, 24 p.Article

A method for initialising the K-means clustering algorithm using kd-treesREDMOND, Stephen J; HENEGHAN, Conor.Pattern recognition letters. 2007, Vol 28, Num 8, pp 965-973, issn 0167-8655, 9 p.Article

Combining multiple classifications of chemical structures using consensus clustering : Chemoinformatics in Drug DiscoveryCHU, Chia-Wei; HOLLIDAY, John D; WILLETT, Peter et al.Bioorganic & medicinal chemistry. 2012, Vol 20, Num 18, pp 5366-5371, issn 0968-0896, 6 p.Article

Centroid index: Cluster level similarity measureFRÄNTI, Pasi; REZAEI, Mohammad; QINPEI ZHAO et al.Pattern recognition. 2014, Vol 47, Num 9, pp 3034-3045, issn 0031-3203, 12 p.Article

A genetic algorithm that exchanges neighboring centers for k-means clusteringLASZLO, Michael; MUKHERJEE, Sumitra.Pattern recognition letters. 2007, Vol 28, Num 16, pp 2359-2366, issn 0167-8655, 8 p.Article

A discrepancy measure for improved clusteringGUPTA, L; TAMMANA, R.Pattern recognition. 1995, Vol 28, Num 10, pp 1627-1634, issn 0031-3203Article

Modified Fuzzy Gap Statistic for Estimating Preferable Number of Clusters in Fuzzy k-Means ClusteringARIMA, Chinatsu; HAKAMADA, Kazumi; OKAMOTO, Masahiro et al.Journal of bioscience and bioengineering. 2008, Vol 105, Num 3, pp 273-281, issn 1389-1723, 9 p.Article

How fast is κ-means?DASGUPTA, Sanjoy.Lecture notes in computer science. 2003, issn 0302-9743, isbn 3-540-40720-0, p. 735Conference Paper

Two classification methods for developing and interpreting productivity zones using site propertiesMARTIN, Nicolas; BOLLERO, German; KITCHEN, Newell R et al.Plant and soil. 2006, Vol 288, Num 1-2, pp 357-371, issn 0032-079X, 15 p.Article

A proposal for robust curve clusteringGARCIA-ESCUDERO, Luis Angel; GORDALIZA, Alfonso.Journal of classification. 2005, Vol 22, Num 2, pp 185-201, issn 0176-4268, 17 p.Article

Efficient global clustering using the Greedy Elimination MethodCHAN, Z. S. H; KASABOV, N.Electronics Letters. 2004, Vol 40, Num 25, pp 1611-1612, issn 0013-5194, 2 p.Article

Choosing the Number of Clusters in K-Means ClusteringSTEINLEY, Douglas; BRUSCO, Michael J.Psychological methods. 2011, Vol 16, Num 3, pp 285-297, issn 1082-989X, 13 p.Article

K-means*: Clustering by gradual data transformationMALINEN, Mikko I; MARIESCU-ISTODOR, Radu; FRÄNTI, Pasi et al.Pattern recognition. 2014, Vol 47, Num 10, pp 3376-3386, issn 0031-3203, 11 p.Article

Robust level set image segmentation via a local correntropy-based K-means clusteringLINGFENG WANG; CHUNHONG PAN.Pattern recognition. 2014, Vol 47, Num 5, pp 1917-1925, issn 0031-3203, 9 p.Article

A clustering method of bloggers based on social annotations : INTELLIGENT, DISTRIBUTED AND PARALLEL COMPUTING AND DATA MANAGEMENTSAKURAI, Shigeaki; TSUTSUI, Hideki.International journal of business intelligence and data mining (Print). 2011, Vol 6, Num 1, pp 26-41, issn 1743-8187, 16 p.Article

A fast k-means clustering algorithm using cluster center displacementLAI, Jim Z. C; HUANG, Tsung-Jen; LIAW, Yi-Ching et al.Pattern recognition. 2009, Vol 42, Num 11, pp 2551-2556, issn 0031-3203, 6 p.Article

A new unsupervised method for document clustering by using WordNet lexical and conceptual relationsREFORGIATO RECUPERO, Diego.Information retrieval (Boston). 2007, Vol 10, Num 6, pp 563-579, issn 1386-4564, 17 p.Article

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