000			03420nam a22003255i 4500
001			103969
003			DE-He213
005			20230102113153.0
007			cr nn 008mamaa
008			141107s2015 gw | s |||| 0|eng d
020			_a9783319092591
024	7		_a10.1007/978-3-319-09259-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC
050		4	_aTK5105.7 2015 EB
245	1	0	_aPartitional Clustering Algorithms _cedited by M. Emre Celebi.
264		1	_aCham _bSpringer International Publishing _c2015
300			_a1 recurso en línea (X, 415 páginas 78 ilustraciones, 45 ilustraciones a color.)
505	0		_aRecent developments in model-based clustering with applications -- Accelerating Lloyd's algorithm for k-means clustering -- Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm -- Nonsmooth optimization based algorithms in cluster analysis -- Fuzzy Clustering Algorithms and Validity Indices for Distributed Data -- Density Based Clustering: Alternatives to DBSCAN -- Nonnegative matrix factorization for interactive topic modeling and document clustering -- Overview of overlapping partitional clustering methods -- On Semi-Supervised Clustering -- Consensus of Clusterings based on High-order Dissimilarities -- Hubness-Based Clustering of High-Dimensional Data -- Clustering for Monitoring Distributed Data Streams.
520	3		_aThis book summarizes the state-of-the-art in partitional clustering. Clustering, the unsupervised classification of patterns into groups, is one of the most important tasks in exploratory data analysis. Primary goals of clustering include gaining insight into, classifying, and compressing data. Clustering has a long and rich history that spans a variety of scientific disciplines including anthropology, biology, medicine, psychology, statistics, mathematics, engineering, and computer science. As a result, numerous clustering algorithms have been proposed since the early 1950s. Among these algorithms, partitional (nonhierarchical) ones have found many applications, especially in engineering and computer science. This book provides coverage of consensus clustering, constrained clustering, large scale and/or high dimensional clustering, cluster validity, cluster visualization, and applications of clustering. Examines clustering as it applies to large and/or high-dimensional data sets commonly encountered in realistic applications; Discusses algorithms specifically designed for partitional clustering; Covers center-based, competitive learning, density-based, fuzzy, graph-based, grid-based, metaheuristic, and model-based approaches.
650		7	_aAnálisis Cluster _9669583 _2embne
650		7	_aAlgoritmos computacionales _2embne _9151819
700	1		_aCelebi, M. Emre _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _0http://id.loc.gov/authorities/names/nb2013000852 _1http://viaf.org/viaf/294998885/ _997651
776	0	8	_iEdición impresa: _z9783319092607
776	0	8	_iEdición impresa: _z9783319092584
776	0	8	_iEdición impresa: _z9783319347981
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-09259-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
490	0		_aEngineering (Springer-11647)
988			_aEBSPRINGER_2018
998			_b06/2019 _dz _ek _feng _ggw _h0
999			_c103969 _d103969 _x1