By Johannes Fürnkranz, Tobias Scheffer, Myra Spiliopoulou
This publication constitutes the refereed complaints of the tenth eu convention on rules and perform of data Discovery in Databases, PKDD 2006, held in Berlin, Germany in September 2006, together with ECML 2006.
The 36 revised complete papers and 26 revised brief papers offered including abstracts of five invited talks have been rigorously reviewed and chosen from 564 papers submitted to either, ECML and PKDD. The papers current a wealth of recent leads to wisdom discovery in databases and handle all present matters within the area.
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Additional resources for Knowledge Discovery in Databases: PKDD 2006: 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, Berlin, Germany,
Further, we believe that the methodology presented here is applicable to web document collections where similar link constraints can be observed. One example is hierarchical clustering of documents where lower level taxonomies may not exhibit strong connectivity. Another application domain is search engine result clustering , an often employed technique to facilitate users’ quick browsing through search results. Both applications suffer from the lack of sufficient links between the documents in a given subspace of the entire collection, which can be addressed by the algorithm proposed here.
An Algorithm for Multi-Relational Discovery of Subgroups. In: Proc. 1st European Symposium on Principles of Data Mining and Knowledge Discovery (PKDD-97), Berlin, Springer Verlag (1997) 78–87 4. : Exploiting Background Knowledge for Knowledge-Intensive Subgroup Discovery. In: Proc. 19th Intl. Joint Conference on Artificial Intelligence (IJCAI-05), Edinburgh, Scotland (2005) 647–652 5. : Subgroup Discovery with CN2-SD. Journal of Machine Learning Research 5 (2004) 153–188 6. : Bump Hunting in High-Dimensional Data.
In the SD-Map algorithm, we just count for each node if the target variable occurs (incrementing tp) or not (incrementing fp), restricted to cases for which the target variable has a defined value. The counts in the general population can then be acquired as a by-product. For handling missing values we propose to construct a second FP-tree-structure, the Missing-FP-tree. The FP-tree for counting the missing values can be restricted to the set of frequent attributes of the main FP-Tree, since only these can form subgroup descriptions that later need to be checked with respect to missing values.