||The Second International Workshop on Mining
Complex Data - MCD'06 -
In Conjunction with IEEE ICDM'06
Hong Kong, 18 - 22 December 2006
This workshop is supported by IASC
Call for papers(PDF version)
Data mining and knowledge discovery can today be considered as stable fields with numerous efficient methods and studies that have been proposed to extract knowledge from data. Nevertheless, the famous golden nugget is still challenging. Actually, the context evolved since the first definition of the KDD process and knowledge has now to be extracted from data getting more and more complex. The structure of the data, for instance, doesn’t match the attribute-value format when considering the web, texts or videos.
In the framework of Data Mining, many software solutions were developed for the extraction of knowledge from tabular data (which are typically obtained from relational databases). Methodological extensions were proposed to deal with data initially obtained from other sources, like in the context of natural language (text mining) and image (image mining). KDD has thus evolved following a unimodal scheme instantiated according to the type of the underlying data (tabular data, text, images, etc), which, in the end, always leads to working on the classical double entry tabular format.
However, in a large number of application domains, this unimodal approach appears to be too restrictive. Consider for instance a corpus of medical files. Each file can contain tabular data such as results of biological analyzes, textual data coming from clinical reports, image data such as radiographies, echograms, or electrocardiograms. In a decision making framework, treating each type of information separately has serious drawbacks. It appears therefore more and more necessary to consider these different data simultaneously, thereby encompassing all their complexity. Many examples of complex data can thus be found in potential knowledge extraction processes. These data can be:
Hence, a natural question arises: how could one combine information of different nature and associate them with a same semantic unit, which is for instance the patient? On a methodological level, one could also wonder how to compare such complex units via similarity measures. The classical approach consists in aggregating partial dissimilarities computed on components of the same type. However, this approach tends to make superposed layers of information. It considers that the whole entity is the sum of its components. By analogy with the analysis of complex systems, it appears that knowledge discovery in complex data can not simply consist of the concatenation of the partial information obtained from each part of the object. The aim would rather be to discover more « global » knowledge giving a meaning to the components and associating them with the semantic unit. This fundamental information cannot be extracted by the currently considered approaches and the available tools.
The new data mining strategies shall take into account the specificities of complex objects (units with which are associated the complex data). These specificities are summarized hereafter:
The difficulty of Knowledge Discovery in complex data lies in all these specificities.
The aim of this workshop is to address issues related to the concept of mining complex data. The whole knowledge discovery process being involved, our goal will be to attract papers dealing with each step of this process. Actually, managing complex data within the KDD process implies to work on every step, starting from the pre-processing (e.g. structuring and organizing) to the visualization and interpretation (e.g. sorting or filtering) of the results, via the data mining methods themselves (e.g. classification, clustering, frequent patterns extraction, etc.). Papers are invited in all KDD fields that involve complex data, including, but not limited to:
The workshop will consist in a series of communications (oral presentations or poster). A reasonable time will be left for the discussion after each presentation. All the articles will be reviewed by at least two references with a double aim of improving quality and giving advice to the authors. A dedicated place will be given to the young researchers with a session (Position paper) grouping the works in progress in various teams. This can be a good occasion for a PhD student or a young researcher to present his/her starting project. This session will be particularly significant for works on their beginning and for the establishment of research groups on shared topics. Demonstrations of research results could be associated with the poster presentations.
The submitted manuscript should closely reflect the final paper as it will appear in the Proceedings.
Papers should not exceed 8 pages (pdf or MS-Word) in the IEEE 2-column format (see the IEEE Computer Society Press Proceedings Author Guidelines )
Submitted papers will be evaluated by at least two reviewers. Any submission that exceeds length limits or deviates from formatting requirements may be rejected without review.
The PDF version of the call for papers is available here