Partitional Clustering Algorithms
This 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. Clus...
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| Format: | eBook |
| Language: | English |
| Published: |
Cham
Springer International Publishing
2015, 2015
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| Edition: | 1st ed. 2015 |
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| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- 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