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Partitional Clustering Algorithms
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Artikel-Nr:
9783319092584
Veröffentl:
2014
Einband:
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Erscheinungsdatum:
20.11.2014
Seiten:
428
Autor:
M. Emre Celebi
Gewicht:
805 g
Format:
241x160x29 mm
Sprache:
Englisch
Beschreibung:
Dr. Emre Celebi is an Associate Professor with the Department of Computer Science, at Louisiana State University in Shreveport.
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.
Examines clustering as it applies to large and/or high-dimensional data sets commonly encountered in real-world applications
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.