Partitional Clustering Algorithms
Springer International Publishing (Verlag)
978-3-319-34798-1 (ISBN)
Dr. Emre Celebi is an Associate Professor with the Department of Computer Science, at Louisiana State University in Shreveport.
Recent 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.
"The content of the book is really outstanding in terms of the clarity of the discourse and the variety of well-selected examples. ... The book brings substantial contributions to the field of partitional clustering from both the theoretical and practical points of view, with the concepts and algorithms presented in a clear and accessible way. It addresses a wide range of readers, including scientists, students, and researchers." (L. State, Computing Reviews, April, 2015)
Erscheinungsdatum | 03.08.2016 |
---|---|
Zusatzinfo | X, 415 p. 78 illus., 45 illus. in color. |
Verlagsort | Cham |
Sprache | englisch |
Maße | 155 x 235 mm |
Themenwelt | Technik ► Elektrotechnik / Energietechnik |
Technik ► Nachrichtentechnik | |
Schlagworte | Center Based Clustering • Communications Engineering, Networks • Engineering • Flat Clustering • Fuzzy c-means • Information Systems and Communication Service • K-means • Nonhierarchical Clustering • Objective Function Based Clustering • Partitional Clustering • Signal, Image and Speech Processing • unsupervised classification • Unsupervised Learning |
ISBN-10 | 3-319-34798-5 / 3319347985 |
ISBN-13 | 978-3-319-34798-1 / 9783319347981 |
Zustand | Neuware |
Haben Sie eine Frage zum Produkt? |
aus dem Bereich