Model Based Clustering And Classification Essay

Extended Paper Submission


Extended papers can be submitted for publication in a Special Issue of the Journal ADAC - Advances in Data Analysis and Classification, on
Advances in Model-Based Clustering and Classification,
Guest Editors: Sylvia Frühwirth-Schnatter, Salvatore Ingrassia, Agustín Mayo-Iscar.


Model-based Clustering and Classification is an increasingly active area in both theoretical and applied research. This area includes probabilistic models for classifying and clustering data, mixture models, statistical learning methods for data classification, thus producing partitions, coverings, fuzzy partitions, hierarchies, in the framework of the supervised and unsupervised classification. The potential of such modeling approaches has become more and more apparent in the very last decades. The increasing tendency now to collect datasets with a large number of variables of both numerical and categorical type in almost any area of scientific research, provides new challenges in model-based clustering and necessitates the development of new approaches that take into account the extraction of the essential features and are able to represent underlying hidden structures and relations in data. Advances in this field should provide a principled statistical approach to a variety of still open and crucial questions like variable selection, robust classification, the choice of the number of model components, cluster recovery from noisy data, robust estimation of parameters, efficient algorithms for parameter estimation, etc. Topics of particular interest may include, but are not limited to:
  • Methodological innovations in all fields of model-based classification and clustering;
  • Developments and applications of model-based classification and clustering methods in specific domains such as bioinformatics, biostatistics, business, data mining, finance, image analysis, machine learning, marketing, medicine, pattern recognition, etc.;
  • Development of specific computational and graphical tools;
  • Robust modeling for data classification.
Researchers and practitioners are kindly requested to submit relevant and innovative papers for publication in this Special Issue. Submitted papers must contain original unpublished work that has not been submitted for publication elsewhere. All manuscripts submitted to this Special Issue will undergo the classical double-blind reviewing process.
Submission details. Papers should be written in LaTeX and not exceed 20 pages (A4 or Letter size with 12 point, fully double-spaced font), including illustrations and tables. The front page of the manuscript must contain a concise and informative title, the names, affiliations, and addresses of all the authors, the e-mail address, telephone, and fax number of the corresponding author, an abstract of 150 to 250 words, and 4 to 6 keywords which can be used for indexing purposes. Further formatting instructions are given on the journal's homepage http://www.springer.com/journal/11634. Manuscripts should be submitted by the electronic submission system from Springer's ADAC website www.springer.com/11634, 'Submit online'. Important dates:
  • Submission of full papers for the Special Issue: November 30, 2016 (earlier submission is encouraged).
  • Notification to authors: March 31, 2017 (tentative).
  • Final papers: June 30, 2017 (tentative).

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