Celebi Partitional Clustering Algorithms

2015
ISBN: 978-3-319-09259-1
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 420 Seiten, eBook

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.

Zielgruppe

Research

Autoren/Hrsg.

Celebi, M. Emre

Inhaltsverzeichnis

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.

Über Autor(innen)

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