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PCA 2014 : Partitional Clustering Algorithms (Springer, 2014)


When Oct 15, 2013 - Apr 1, 2014
Where N/A
Abstract Registration Due Oct 15, 2013
Submission Deadline Jan 15, 2014
Notification Due Mar 1, 2014
Final Version Due Apr 1, 2014
Categories    data mining   clustering   unsupervised learning

Call For Papers

Dear Colleagues,

We would like to invite you to contribute a chapter for our upcoming volume
entitled Partitional Clustering Algorithms to be published by Springer
sometime in late 2014.

Below is a short description of the volume:

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.
Clustering has a long and rich history that spans a variety of scientific
disciplines including anthropology, biology, medicine, psychology, statistics,
mathematics, engineering, and computer science. As a result, numerous clustering
algorithms have been proposed since the early 1950s. Among these algorithms,
partitional (nonhierarchical) ones have found many applications, especially in
engineering and computer science.

The goal of this volume is to summarize the state-of-the-art in partitional
clustering. Topics of interest include:

- Competitive learning clustering
- Density-based clustering
- Fuzzy/Possibilistic/Probabilistic clustering
- Graph-based clustering
- Grid-based clustering
- Metaheuristic clustering
- Model-based clustering
- Consensus clustering
- Constrained clustering
- Clustering large and/or high-dimensional data
- Cluster validity
- Cluster visualization
- Applications

Important Dates
Submission of abstracts October 15, 2013
Notification of initial editorial decisions November 1, 2013
Submission of full-length chapters January 15, 2014
Notification of final editorial decisions March 1, 2014
Submission of revised chapters April 1, 2014

All submissions should be done via EasyChair:
Original artwork and a signed copyright release form will be required for all
accepted chapters. For author instructions, please visit:

Feel free to contact us via email (‘ecelebi AT lsus DOT edu’) regarding your chapter ideas.


M. Emre Celebi, Ph.D.
Associate Professor
Department of Computer Science
Louisiana State University in Shreveport

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