The 9th International Conference on Discovery Science (DS 2006) was held in Barcelona, Spain, on 7–10 October 2006. The conference was collocated with the 17th International Conference on Algorithmic Learning Theory (ALT 2006). The two conferences shared the invited talks. This LNAI volume, containing the proceedings of the 9th International C- ference onDiscoveryScience, is structured in three parts. The ?rstpart contains the papers/abstracts of the invited talks, the second part contains the accepted long papers, and the third part the accepted regular (short) papers. Out of 87 submitted papers, 23 were accepted for publication as long papers, and 18 as regular papers. All the submitted papers were reviewed by two or three ref- ees. In addition to the presentations of accepted papers, the DS 2006 conference program consisted of three invited talks, two tutorials, the collocated ALT 2006 conference and the Pascal Dialogues workshop. We wish to express our gratitude to – the authors of submitted papers, – the program committee and other referees for their thorough and timely paper evaluation, – DS 2006 invited speakers Carole Goble and Padhraic Smyth, as well as - drew Ng as joint DS 2006 and ALT 2006 invited speaker, – invited tutorial speakers Luis Torgo and Michael May, – the local organization committee chaired by Ricard Gavalda, ` – DS 2006 conference chair Klaus P.
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This book constitutes the refereed proceedings of the 9th International Conference on Discovery Science, DS 2006, held in Barcelona, Spain in October 2006, co-located with the 17th International Conference on Algorithmic Learning Theory, ALT 2006. The 23 revised long papers and the 18 revised regular papers presented together with 5 invited papers were carefully reviewed and selected from 87 submissions. The papers cover all issues in the area of automating scientific discovery or working on tools for supporting the human process of discovery in science. They address theoretical and methodological topics such as intelligent data analysis, knowledge discovery and machine learning, as well as their application to scientific knowledge discovery.
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