The SCEAS System
Navigation Menu
Introduction
Collection Based
Clustering
Ranking
Paper Based
Journals
Conferences
DB Status
Statistics
Search

Search the dblp DataBase

Title:
Author:
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]


Search in 0.005secs, Finished in 0.007secs
NOTICE1
System may not be available sometimes or not working properly, since it is still in development with continuous upgrades
NOTICE2
The rankings that are presented on this page should NOT be considered as formal since the citation info is incomplete in DBLP
 
Introduction Collection Based Clustering Ranking Paper Based Journals Conferences DB Status Statistics Search
System created by asidirop@csd.auth.gr [http://users.auth.gr/~asidirop/] © 2002
for Data Engineering Laboratory, Department of Informatics, Aristotle University © 2002