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Einoshin Suzuki: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Yuu Yamada, Einoshin Suzuki, Hideto Yokoi, Katsuhiko Takabayashi
    Experimental Evaluation of Time-Series Decision Tree. [Citation Graph (0, 0)][DBLP]
    Active Mining, 2003, pp:190-209 [Conf]
  2. Einoshin Suzuki
    Undirected Exception Rule Discovery as Local Pattern Detection. [Citation Graph (0, 0)][DBLP]
    Local Pattern Detection, 2004, pp:207-216 [Conf]
  3. Masayuki Hirose, Einoshin Suzuki
    Using WWW-Distribution of Words in Detecting Peculiar Web Pages. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2004, pp:355-362 [Conf]
  4. Yukihiro Nakamura, Shin Ando, Kenji Aoki, Hiroyuki Mano, Einoshin Suzuki
    Strategy Diagram for Identifying Play Strategies in Multi-view Soccer Video Data. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2006, pp:173-184 [Conf]
  5. Jérôme Maloberti, Einoshin Suzuki
    Improving Efficiency of Frequent Query Discovery by Eliminating Non-relevant Candidates. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2003, pp:220-232 [Conf]
  6. Masaki Narahashi, Einoshin Suzuki
    Subspace Clustering Based on Compressibility. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2002, pp:435-440 [Conf]
  7. Shinsuke Sugaya, Einoshin Suzuki
    Normal Form Transformation for Object Recognition Based on Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 1999, pp:306-315 [Conf]
  8. Einoshin Suzuki
    Issues in Organizing a Successful Knowledge Discovery Contest. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2000, pp:282-284 [Conf]
  9. Einoshin Suzuki
    Worst-Case Analysis of Rule Discovery. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2001, pp:365-377 [Conf]
  10. Einoshin Suzuki
    Scheduled Discovery of Exception Rules. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 1999, pp:184-195 [Conf]
  11. Einoshin Suzuki
    In Pursuit of Interesting Patterns with Undirected Discovery of Exception Rules. [Citation Graph (0, 0)][DBLP]
    Progress in Discovery Science, 2002, pp:504-517 [Conf]
  12. Jérôme Maloberti, Shin Ando, Einoshin Suzuki
    Classification non-supervisée de données relationnelles. [Citation Graph (0, 0)][DBLP]
    EGC, 2006, pp:389-390 [Conf]
  13. Régis Gras, Pascale Kuntz, Einoshin Suzuki
    Une règle d'exception en Analyse Statistique Implicative. [Citation Graph (0, 0)][DBLP]
    EGC, 2007, pp:87-98 [Conf]
  14. Einoshin Suzuki
    Peut-on Capturer la Sémantique à Travers la Syntaxe ? - Découverte des Règles d'Exception Simultanée. [Citation Graph (0, 0)][DBLP]
    EGC, 2007, pp:1- [Conf]
  15. Pierre Morizet-Mahoudeaux, Einoshin Suzuki, Setsuo Ohsuga
    Knowledge-Based Handling of Design Expertise. [Citation Graph (0, 4)][DBLP]
    ICDE, 1994, pp:368-374 [Conf]
  16. Einoshin Suzuki, Takeshi Watanabe, Hideto Yokoi, Katsuhiko Takabayashi
    Detecting Interesting Exceptions from Medical Test Data with Visual Summarization. [Citation Graph (0, 0)][DBLP]
    ICDM, 2003, pp:315-322 [Conf]
  17. Shin Ando, Einoshin Suzuki
    An Information Theoretic Approach to Detection of Minority Subsets in Database. [Citation Graph (0, 0)][DBLP]
    ICDM, 2006, pp:11-20 [Conf]
  18. Fumio Takechi, Einoshin Suzuki
    Finding an Optimal Gain-Ratio Subset-Split Test for a Set-Valued Attribute in Decision Tree Induction. [Citation Graph (0, 0)][DBLP]
    ICML, 2002, pp:618-625 [Conf]
  19. Yuu Yamada, Einoshin Suzuki, Hideto Yokoi, Katsuhiko Takabayashi
    Decision-tree Induction from Time-series Data Based on a Standard-example Split Test. [Citation Graph (0, 0)][DBLP]
    ICML, 2003, pp:840-847 [Conf]
  20. Jérôme Maloberti, Einoshin Suzuki
    An Efficient Algorithm for Reducing Clauses Based on Constraint Satisfaction Techniques. [Citation Graph (0, 0)][DBLP]
    ILP, 2004, pp:234-251 [Conf]
  21. Marie Agier, Jean-Marc Petit, Einoshin Suzuki
    Towards Ad-Hoc Rule Semantics for Gene Expression Data. [Citation Graph (0, 0)][DBLP]
    ISMIS, 2005, pp:494-503 [Conf]
  22. Nicolas Durand, Bruno Crémilleux, Einoshin Suzuki
    Visualizing Transactional Data with Multiple Clusterings for Knowledge Discovery. [Citation Graph (0, 0)][DBLP]
    ISMIS, 2006, pp:47-57 [Conf]
  23. Shutaro Inatani, Einoshin Suzuki
    Data Squashing for Speeding Up Boosting-Based Outlier Detection. [Citation Graph (0, 0)][DBLP]
    ISMIS, 2002, pp:601-612 [Conf]
  24. Masatoshi Jumi, Einoshin Suzuki, Muneaki Ohshima, Ning Zhong, Hideto Yokoi, Katsuhiko Takabayashi
    Multi-strategy Instance Selection in Mining Chronic Hepatitis Data. [Citation Graph (0, 0)][DBLP]
    ISMIS, 2005, pp:475-484 [Conf]
  25. Einoshin Suzuki
    Autonomous Discovery of Reliable Exception Rules. [Citation Graph (0, 0)][DBLP]
    KDD, 1997, pp:259-262 [Conf]
  26. Einoshin Suzuki
    Simultaneous Reliability Evaluation of Generality and Accuracy for Rule Discovery in Databases. [Citation Graph (0, 0)][DBLP]
    KDD, 1998, pp:339-343 [Conf]
  27. Einoshin Suzuki, Masamichi Shimura
    Exceptional Knowledge Discovery in Databases Based on Information Theory. [Citation Graph (0, 0)][DBLP]
    KDD, 1996, pp:275-278 [Conf]
  28. Farhad Hussain, Huan Liu, Einoshin Suzuki, Hongjun Lu
    Exception Rule Mining with a Relative Interestingness Measure. [Citation Graph (0, 0)][DBLP]
    PAKDD, 2000, pp:86-97 [Conf]
  29. Einoshin Suzuki, Toru Ohno
    Prediction Rule Discovery Based on Dynamic Bias Selection. [Citation Graph (0, 0)][DBLP]
    PAKDD, 1999, pp:504-508 [Conf]
  30. Einoshin Suzuki, Shusaku Tsumoto
    Evaluating Hypothesis-Driven Exception-Rule Discovery with Medical Data Sets. [Citation Graph (0, 0)][DBLP]
    PAKDD, 2000, pp:208-211 [Conf]
  31. Yuta Choki, Einoshin Suzuki
    Iterative Data Squashing for Boosting Based on a Distribution-Sensitive Distance. [Citation Graph (0, 0)][DBLP]
    PKDD, 2002, pp:86-98 [Conf]
  32. Shinsuke Sugaya, Einoshin Suzuki, Shusaku Tsumoto
    Support Vector Machines for Knowledge Discovery. [Citation Graph (0, 0)][DBLP]
    PKDD, 1999, pp:561-567 [Conf]
  33. Einoshin Suzuki, Masafumi Gotoh, Yuta Choki
    Bloomy Decision Tree for Multi-objective Classification. [Citation Graph (0, 0)][DBLP]
    PKDD, 2001, pp:436-447 [Conf]
  34. Einoshin Suzuki, Yves Kodratoff
    Discovery of Surprising Exception Rules Based on Intensity of Implication. [Citation Graph (0, 0)][DBLP]
    PKDD, 1998, pp:10-18 [Conf]
  35. Einoshin Suzuki, Jan M. Zytkow
    Unified Algorithm for Undirected Discovery of Execption Rules. [Citation Graph (0, 0)][DBLP]
    PKDD, 2000, pp:169-180 [Conf]
  36. David Ramamonjisoa, Einoshin Suzuki, Issam A. Hamid
    Research Topics Discovery from WWW by Keywords Association Rules. [Citation Graph (0, 0)][DBLP]
    Rough Sets and Current Trends in Computing, 2000, pp:412-419 [Conf]
  37. Einoshin Suzuki, Hiroki Ishihara
    Visualizing Discovered Rule Sets with Visual Graphs Based on Compressed Entropy Density. [Citation Graph (0, 0)][DBLP]
    RSFDGrC, 1999, pp:414-422 [Conf]
  38. Masaki Narahashi, Einoshin Suzuki
    Detecting Hostile Accesses through Incremental Subspace Clustering. [Citation Graph (0, 0)][DBLP]
    Web Intelligence, 2003, pp:337-343 [Conf]
  39. Masanori Yoshinaga, Yukihiro Nakamura, Einoshin Suzuki
    Mini-Car-Soccer as a testbed for granular computing. [Citation Graph (0, 0)][DBLP]
    GrC, 2005, pp:92-97 [Conf]
  40. Einoshin Suzuki
    Worst Case and a Distribution-Based Case Analyses of Sampling for Rule Discovery Based on Generality and Accuracy. [Citation Graph (0, 0)][DBLP]
    Appl. Intell., 2005, v:22, n:1, pp:29-36 [Journal]
  41. Einoshin Suzuki, Jan M. Zytkow
    Unified algorithm for undirected discovery of exception rules. [Citation Graph (0, 0)][DBLP]
    Int. J. Intell. Syst., 2005, v:20, n:7, pp:673-691 [Journal]
  42. Einoshin Suzuki
    Undirected Discovery of Interesting Exception Rules. [Citation Graph (0, 0)][DBLP]
    IJPRAI, 2002, v:16, n:8, pp:1065-1086 [Journal]
  43. Einoshin Suzuki
    Data Mining Methods for Discovering Interesting Exceptions from an Unsupervised Table. [Citation Graph (0, 0)][DBLP]
    J. UCS, 2006, v:12, n:6, pp:627-653 [Journal]
  44. Tatsuya Akutsu, Einoshin Suzuki, Setsuo Ohsuga
    Logic-based approach to expert systems in chemistry. [Citation Graph (0, 0)][DBLP]
    Knowl.-Based Syst., 1991, v:4, n:2, pp:103-116 [Journal]
  45. Einoshin Suzuki, Tatsuya Akutsu, Setsuo Ohsuga
    Knowledge-based system for computer-aided drug design. [Citation Graph (0, 0)][DBLP]
    Knowl.-Based Syst., 1993, v:6, n:2, pp:114-126 [Journal]
  46. Shin Ando, Einoshin Suzuki, Shigenobu Kobayashi
    Sample based crowding method for multimodal optimization in continuous domain. [Citation Graph (0, 0)][DBLP]
    Congress on Evolutionary Computation, 2005, pp:1867-1874 [Conf]
  47. Marie Agier, Jean-Marc Petit, Einoshin Suzuki
    Unifying Framework for Rule Semantics: Application to Gene Expression Data. [Citation Graph (0, 0)][DBLP]
    Fundam. Inform., 2007, v:78, n:4, pp:543-559 [Journal]

  48. Discovering Community-Oriented Roles of Nodes in a Social Network. [Citation Graph (, )][DBLP]


  49. Finding the k-Most Abnormal Subgraphs from a Single Graph. [Citation Graph (, )][DBLP]


  50. Unsupervised Cross-Domain Learning by Interaction Information Co-clustering. [Citation Graph (, )][DBLP]


  51. Compression-Based Measures for Mining Interesting Rules. [Citation Graph (, )][DBLP]


  52. Detection of unique temporal segments by information theoretic meta-clustering. [Citation Graph (, )][DBLP]


  53. Negative Encoding Length as a Subjective Interestingness Measure for Groups of Rules. [Citation Graph (, )][DBLP]


  54. Discovering Action Rules That Are Highly Achievable from Massive Data. [Citation Graph (, )][DBLP]


  55. Subclass-Oriented Dimension Reduction with Constraint Transformation and Manifold Regularization. [Citation Graph (, )][DBLP]


  56. Mining Peculiar Compositions of Frequent Substrings from Sparse Text Data Using Background Texts. [Citation Graph (, )][DBLP]


  57. Semi-supervised Projection Clustering with Transferred Centroid Regularization. [Citation Graph (, )][DBLP]


  58. Intuitive Display for Search Engines Toward Fast Detection of Peculiar WWW Pages. [Citation Graph (, )][DBLP]


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