Participants intending to present a talk are invited to submit an abstract. All abstracts will undergo a reviewing process. Accepted abstracts will be distributed to the conference participants.
Authors of accepted abstracts may submit a full paper (contributed papers up to 10 pages; key-note lectures up to 14 pages). Please note that all full papers will also undergo a reviewing process. Accepted papers will be published in the Springer Series “Studies in Classification, Data Analysis, and Knowledge Organization” which will be released in 2014. Please find the guidelines for writing a full paper here.
The broad range of relevant topics is illustrated by the following list of intended sections. Contributed papers from scholars and practitioners are invited on any of these as well as on related topics:
- Theory and Methods, including but not limited to Multivariate Methods, Exploratory Data Analysis, Clustering and Classification, Pattern Recognition and Machine Learning, Visualization and Scaling Methods, Evaluation of Methods
- Data Science, including Data Pre-Processing, Text and Web Mining, Information Extraction and Retrieval, Personalization and Intelligent Agents
- Applications, involving Marketing and Management Science, Banking and Finance, Production, Controlling and OR, Biostatistics and Bioinformatics, Genome and DNA Analysis, Medical and Health Sciences, Archaeology and Geography, Linguistics and Statistical Musicology, Psychology and Education, Library Science
- The Workshop on Library and Information Science solicits contributions on the role of classification and data analysis in this domain. Topics in this area include but are not limited to: Classification and subject indexing in the context of catalogs and resource discovery systems; Methods, approaches and applications in subject indexing, classification and data analysis in different countries; Open access to classification systems: How can we provide a sustainable classification infrastructure?; Linked (subject) data (e.g. faceted classification and linked data architectures: A happy alignment?); Classification, subject indexing and the semantic web (e.g. taxonomies and semantic web ontologies: How closely are they related to each other?); Automatic and manual methods in classification and subject indexing (e.g. mappings, concordances, heuristics); Subject retrieval in multilingual, multicultural environments; Serendipity in library collections and digital libraries
Please note that during online submission you will be asked to assign your submission to one of the following conference areas:
- Statistics and Data Analysis
- Machine Learning and Knowledge Discovery
- Data Analysis in Marketing
- Data Analysis in Finance
- Data Analysis in Biostatistics and Bioinformatics
- Data Analysis in Interdisciplinary Domains
Please find the guidelines for writing an abstract here, and for writing a full paper here.