The 6th International Conference on Discovery Science Hokkaido University Conference Hall, Sapporo, Japan October 17-19, 2003

List of accepted papers

Long papers

• Dialectical Evidence Assembly for Discovery
  Alistair Fletcher and John Davis
• Clustering Orders
  Toshihiro Kamishima and Jun Fujiki
• Mining Frequent Itemsets with Category-based Constraints
  Tien Dung Do, Siu Cheung Hui, and Alvis Fong
• Chaining Patterns
  Taneli Milikäinen
• Performance Analysis of a Greedy Algorithm for Inferring Boolean Functions
  Daiji Fukagawa and Tatsuya Akutsu
• Enumerating Maximal Frequent Sets using Irredundant Dualization
  Ken Satoh Takeaki Uno
• Performance Evaluation of Decision Tree Graph-Based Induction
  Warodom Geamsakul, Takashi Matsuda, Tetsuya Yoshida, Hiroshi Motoda, and Takashi Washio
• Improving Efficiency of Frequent Query Discovery by Eliminating Non-relevant Candidates
  Jerome Maloberti and Einoshin Suzuki
• Extraction of Coverings as Monotone DNF Formulas
  Kouichi Hirata, Ryosuke Nagazumi, and Masateru Harao
• An Optimal Strategy for Extracting Probabilistic Rules by Combining Rough Sets and Genetic Algorithm
  Xiaoshu Hang Honghua Dai
• Experimental Study of Discovering Essential Information from Customer Inquiry
  Keiko Shimazu, Atsuhito Momma, and Koichi Furukawa
• Business Application for Sales Transaction Data by Using Genome Analysis Technology
  Naoki Katoh, Katsutoshi Yada, and Yukinobu Hamuro
• Discovering Rich Navigation Patterns on a Web Site
  Karine Chevalier Cecile Bothorel Vincent Corruble
• Discovering Frequent Substructures in Large Unordered Trees
  Tatsuya Asai, Hiroki Arimura, Takeaki Uno, and Shin-ichi Nakano
• Modelling soil radon concentration for earthquake prediction
  Saso D?eroski Ljupco Todorovski Boris Zmazek Janja Vaupotic Ivan Kobal
• Algorithm for Discovery of New Families of Optimal Regular Networks
  O. Monakhov and E. Monakhova
• What Kinds and Amounts of Causal Knowledge Can Be Acquired from Text by Using Connective Markers as Clues?
  Inui Takashi, Inui Kentaro, and Matsumoto Yuji
• Inducing Ecosystem Models from Time Series Data
  Pat Langley, Kazumi Saito, Dileep George, and Stephen Bay

Short papers

• Effects of Unreliable Group Profiling by means of Data Mining
  Bart Custers
• Discovery of Cellular Automata Rules Using Cases
  Ken-ichi Maeda and Chiaki Sakama
• Content-Based Scene Change Detection Video sequence using Hierarchical Hidden Markov Model
  Jong-Hyun Park
• Mining Multiple Clustering Data for Knowledge Discovery
  Thanh Tho Quan, Siu Cheung Hui, and Alvis Fong
• An appraisal of UNIVAUTO--the first discovery program to generate scientific article
  Vladimir Pericliev
• Discovery of Relationships between Interests from Bulletin Board System by Dissimilarity Reconstruction
  Kou Zhongbao, Ban Tao, and Zhang Changshui
• Scilog: A Language for Scientific Processes and Scales
  Joseph Phillips
• Clustering Personal Homepages for Knowledge Discovery
  Toyohisa Nakada and Susumu Kunifuji
• Association Rules and Dempster-Shafer Theory of Evidence
  Tetsuya Murai, Yasuo Kudo, and Yoshiharu Sato
• Mining Interesting Patterns Using Estimated Frequencies from Subpatterns and Superpatterns
  Yukiko Yoshida, Yuiko Ohta, Ken'ichi Kobayashi, and Nobuhiro Yugami
• A Genetic Algorithm for Inferring Pseudoknotted RNA Structures from Sequence Data
  Dongkyu Lee and Kyungsook Han
• Automatic Classification for the Identification of Relationships in a Metadata Repository
  Gerd Beuster, Ulrich Furbach, Margret Gross-Hardt, and Bernd Thomas
• Knowledge Discovery on Chemical Reactivity from Experimental Reaction Information
  Hiroko Satoh and Tadashi Nakata
• Discovery of Web Communities from Positive and Negative Examples
  Tsuyoshi Murata
• SA-Optimized Multiple View Smooth Polyhedron Representation NN
  Mohamad Ivan Fanany and Itsuo Kumazawa
• Mining RNA Structure Elements from the Structure Data of Protein-RNA Complexes
  Daeho Lim and Kyungsook Han
• Discovering Polysemic Words using a N-partite Graph Clustering Hidekazu Nakawatase; Akiko Aizawa
• Discovering Most Classificatory Patterns for Very Expressive Pattern Classes
  Masayuki Takeda, Shunsuke Inenaga, Hideo Bannai, Ayumi Shinohara, and Setsuo Arikawa
• Discovery of explicit and implicit connections in textual information
  Peter Bruza, Robert McArthur, and Dawei Song
• Creating Abstract Concepts for Classification by Finding Top-N Maximal Weighted Cliques
  Yoshiaki Okubo and Makoto Haraguchi
• A Method of Extracting Related Words Using Standardized Mutual Information
  Tomohiko Sugimachi, Akira Ishino, Masayuki Takeda, and Fumihiro Matsuo
• Prediction of Molecular Bioactivity for Drug Design using a Decision Tree Algorithm
  Sanghoon Lee, Jihoon Yang, and Kyung-whan Oh
• Using Constraints in Discovering Dynamics
  Saso Dzeroski Ljupco Todorovski Peter Ljubic
• Bacterium Lingualis - the Web-based Commonsensical Knowledge Discovery Method
  Rafal RZEPKA Kenji ARAKI Koji TOCHINAI
• Collaborative Filtering using Projective Restoration Operators
  Atsuyoshi Nakamura, Mineichi Kudo, Akira Tanaka, and Kazuhiko Tanabe
• Discovery of trends and states in irregular temproal data
  Trong Dung Nguyen and Tu Bao Ho
• Inducing Biologically Plausible Models from Temporal Expression Data
  Pat Langley, Dileep George, Kazumi Saito, Jeff Shrager, and Stephen Bay
• Elements of Agile Discovery Environment
  Peter Grigoriev and Serhiy Yevtushenko
• Creating User-Adapted Design Recommender System through Collaborative Filtering and Content Based Filtering
  Kyung-Yong Jung and Jung-Hyun Lee