Partitional Clustering Algorithms

Herausgegeben:Celebi, M. Emre

• Gebundenes Buch

Produktbeschreibung

This book focuses on partitional clustering algorithms, which are commonly used in engineering and computer scientific applications. The goal of this volume is to summarize the state-of-the-art in partitional clustering. The book includes such topics as center-based clustering, competitive learning clustering and density-based clustering. Each chapter is contributed by a leading expert in the field.

Produktdetails

• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 86290081, 978-3-319-09258-4
• 2015
• Seitenzahl: 428
• Erscheinungstermin: 20. November 2014
• Englisch
• Abmessung: 241mm x 160mm x 29mm
• Gewicht: 826g
• ISBN-13: 9783319092584
• ISBN-10: 3319092588
• Artikelnr.: 41099755

Autorenporträt

Dr. Emre Celebi is an Associate Professor with the Department of Computer Science, at Louisiana State University in Shreveport.

Inhaltsangabe

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.

Rezensionen

"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)