Special Issue on Data Clustering Theory and Applications  

 

Cluster analysis is an unsupervised process that divides a set of objects into homogeneous groups. Data clustering is a state of the art problem with increasing number of applications, such as computer vision and pattern recognition, networks, statistical physics and mechanics, and so on.

 

In this special issue, we intend to invite front-line researchers and authors to submit original research and review articles on exploring data clustering theory and applications. Potential topics include, but are not limited to:

• Data clustering
• Data types
• Scale conversion
• Data standardization and transformation
• Data visualization
• Similarity and dissimilarity measures
• Hierarchical clustering techniques
• Clustering algorithms

 

Authors should read over the journal’s For Authors carefully before submission. Prospective authors should submit an electronic copy of their complete manuscript through the journal’s Paper Submission System.

 

Please kindly specify the “Special Issue” under your manuscript title. The research field “Special Issue - Data Clustering Theory and Applications” should be selected during your submission.

 

Special Issue timetable:

 

 

Guest Editor:

 

For further questions or inquiries

Please contact Editorial Assistant at

am@scirp.org