Volume Editors
Copyright c 2011 for the individual papers by papers' authors, for the Volume by the editors. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means without the prior permission of the copyright owners. Concept discovery is a Knowledge Discovery in Databases (KDD) research eld that uses human-centered techniques such as Formal Concept Analysis (FCA), Biclustering, Triclustering, Conceptual Graphs etc. for gaining insight into the underlying conceptual structure of the data. Traditional machine learning techniques are mainly focusing on structured data whereas most data available resides in unstructured, often textual, form. Compared to traditional data mining techniques, human-centered instruments actively engage the domain expert in the discovery process.
This volume contains the contributions to CDUD 2011, the International Workshop on Concept Discovery in Unstructured Data (CDUD) held in Moscow. The main goal of this workshop was to provide a forum for researchers and developers of data mining instruments working on issues with analyzing unstructured data.
We are proud that we could welcome 13 valuable contributions to this volume. The majority of the accepted papers described innovative research on data discovery in unstructured texts. Authors worked on issues such as transforming unstructured into structured information by amongst others extracting keywords and opinion words from texts with Natural Language Processing methods. Multiple authors who participated in the workshop used methods from the conceptual structures eld including Formal Concept Analysis and Conceptual Graphs. Applications include but are not limited to text mining police reports, sociological de nitions, movie reviews, etc.
Last but not least, we would like to thank the administration of the Higher School of Economics who took care of all arrangements to make this conference pleasant and enjoyable.
Organization This CDUD'11 workshop was held in June 2011 in Moscow, Russia co-located with the 13th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC-2011) at the National Research University Higher School of Economics.
Bernhard Ganter Richard Hill Alex Neznanov Simon Polovina Henrik Scharfe Vladimir Selegey Stijn Viaene Laszlo Szathmary State University Higher School of Economics, Russia State University Higher School of Economics, Russia Katholieke Universiteit Leuven, Belgium
Amsterdam Business School, The Netherlands Amsterdam-Amstelland Police, The Netherlands Dresden University of Technology, Germany University of Derby, UK State University Higher School of Economics, Russia University of She eld, UK Aalborg University, Denmark ABBYY, Russia Katholieke Universiteit Leuven, Belgium University of Quebec in Montreal, Canada
Russian Foundation for Basic Research, Moscow Poncelet Laboratory (UMI 2615 du CNRS), Moscow State University Higher School of Economics, Moscow Yandex, Moscow Witology, Moscow Dynasty Foundation, Moscow