000 03513nam a22004575i 4500
001 978-3-319-09259-1
003 DE-He213
005 20200421112229.0
007 cr nn 008mamaa
008 141107s2015 gw | s |||| 0|eng d
020 _a9783319092591
_9978-3-319-09259-1
024 7 _a10.1007/978-3-319-09259-1
_2doi
050 4 _aTK1-9971
072 7 _aTJK
_2bicssc
072 7 _aTEC041000
_2bisacsh
082 0 4 _a621.382
_223
245 1 0 _aPartitional Clustering Algorithms
_h[electronic resource] /
_cedited by M. Emre Celebi.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _aX, 415 p. 78 illus., 45 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
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 _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 0 _aEngineering.
650 0 _aComputers.
650 0 _aElectrical engineering.
650 1 4 _aEngineering.
650 2 4 _aCommunications Engineering, Networks.
650 2 4 _aInformation Systems and Communication Service.
650 2 4 _aSignal, Image and Speech Processing.
700 1 _aCelebi, M. Emre.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319092584
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-09259-1
912 _aZDB-2-ENG
942 _cEBK
999 _c57862
_d57862