2009 | ||
---|---|---|
62 | JianBin Wang, Bin-Hui Chou, Einoshin Suzuki: Finding the k-Most Abnormal Subgraphs from a Single Graph. Discovery Science 2009: 441-448 | |
61 | Daisuke Ikeda, Einoshin Suzuki: Mining Peculiar Compositions of Frequent Substrings from Sparse Text Data Using Background Texts. ECML/PKDD (1) 2009: 596-611 | |
60 | Einoshin Suzuki: Compression-Based Measures for Mining Interesting Rules. IEA/AIE 2009: 741-746 | |
59 | Shin Ando, Einoshin Suzuki: Detection of unique temporal segments by information theoretic meta-clustering. KDD 2009: 59-68 | |
58 | Einoshin Suzuki: Negative Encoding Length as a Subjective Interestingness Measure for Groups of Rules. PAKDD 2009: 220-231 | |
57 | Einoshin Suzuki: Discovering Action Rules That Are Highly Achievable from Massive Data. PAKDD 2009: 713-722 | |
2008 | ||
56 | Régis Gras, Einoshin Suzuki, Fabrice Guillet, Filippo Spagnolo: Statistical Implicative Analysis, Theory and Applications Springer 2008 | |
55 | Takashi Washio, Einoshin Suzuki, Kai Ming Ting, Akihiro Inokuchi: Advances in Knowledge Discovery and Data Mining, 12th Pacific-Asia Conference, PAKDD 2008, Osaka, Japan, May 20-23, 2008 Proceedings Springer 2008 | |
54 | Shin Ando, Einoshin Suzuki: Unsupervised Cross-Domain Learning by Interaction Information Co-clustering. ICDM 2008: 13-22 | |
53 | Einoshin Suzuki: Pitfalls for Categorizations of Objective Interestingness Measures for Rule Discovery. Statistical Implicative Analysis 2008: 383-395 | |
2007 | ||
52 | Vincent Corruble, Masayuki Takeda, Einoshin Suzuki: Discovery Science, 10th International Conference, DS 2007, Sendai, Japan, October 1-4, 2007, Proceedings Springer 2007 | |
51 | Einoshin Suzuki: Peut-on Capturer la Sémantique à Travers la Syntaxe ? - Découverte des Règles d'Exception Simultanée. EGC 2007: 1 | |
50 | Régis Gras, Pascale Kuntz, Einoshin Suzuki: Une règle d'exception en Analyse Statistique Implicative. EGC 2007: 87-98 | |
49 | Marie Agier, Jean-Marc Petit, Einoshin Suzuki: Unifying Framework for Rule Semantics: Application to Gene Expression Data. Fundam. Inform. 78(4): 543-559 (2007) | |
48 | Masatoshi Jumi, Muneaki Ohshima, Ning Zhong, Hideto Yokoi, Katsuhiko Takabayashi, Einoshin Suzuki: Spiral Removal of Exceptional Patients for Mining Chronic Hepatitis Data. New Generation Comput. 25(3): 223-234 (2007) | |
2006 | ||
47 | Yukihiro Nakamura, Shin Ando, Kenji Aoki, Hiroyuki Mano, Einoshin Suzuki: Strategy Diagram for Identifying Play Strategies in Multi-view Soccer Video Data. Discovery Science 2006: 173-184 | |
46 | Jérôme Maloberti, Shin Ando, Einoshin Suzuki: Classification non-supervisée de données relationnelles. EGC 2006: 389-390 | |
45 | Shin Ando, Einoshin Suzuki: An Information Theoretic Approach to Detection of Minority Subsets in Database. ICDM 2006: 11-20 | |
44 | Nicolas Durand, Bruno Crémilleux, Einoshin Suzuki: Visualizing Transactional Data with Multiple Clusterings for Knowledge Discovery. ISMIS 2006: 47-57 | |
43 | Einoshin Suzuki, Shin Ando, Masayuki Hirose, Masatoshi Jumi: Intuitive Display for Search Engines Toward Fast Detection of Peculiar WWW Pages. WImBI 2006: 341-352 | |
42 | Einoshin Suzuki: Data Mining Methods for Discovering Interesting Exceptions from an Unsupervised Table. J. UCS 12(6): 627-653 (2006) | |
2005 | ||
41 | Shin Ando, Einoshin Suzuki, Shigenobu Kobayashi: Sample based crowding method for multimodal optimization in continuous domain. Congress on Evolutionary Computation 2005: 1867-1874 | |
40 | Masanori Yoshinaga, Yukihiro Nakamura, Einoshin Suzuki: Mini-Car-Soccer as a testbed for granular computing. GrC 2005: 92-97 | |
39 | Masatoshi Jumi, Einoshin Suzuki, Muneaki Ohshima, Ning Zhong, Hideto Yokoi, Katsuhiko Takabayashi: Multi-strategy Instance Selection in Mining Chronic Hepatitis Data. ISMIS 2005: 475-484 | |
38 | Marie Agier, Jean-Marc Petit, Einoshin Suzuki: Towards Ad-Hoc Rule Semantics for Gene Expression Data. ISMIS 2005: 494-503 | |
37 | Einoshin Suzuki: Worst Case and a Distribution-Based Case Analyses of Sampling for Rule Discovery Based on Generality and Accuracy. Appl. Intell. 22(1): 29-36 (2005) | |
36 | Einoshin Suzuki, Jan M. Zytkow: Unified algorithm for undirected discovery of exception rules. Int. J. Intell. Syst. 20(7): 673-691 (2005) | |
2004 | ||
35 | Einoshin Suzuki, Setsuo Arikawa: Discovery Science, 7th International Conference, DS 2004, Padova, Italy, October 2-5, 2004, Proceedings Springer 2004 | |
34 | Masayuki Hirose, Einoshin Suzuki: Using WWW-Distribution of Words in Detecting Peculiar Web Pages. Discovery Science 2004: 355-362 | |
33 | Jérôme Maloberti, Einoshin Suzuki: An Efficient Algorithm for Reducing Clauses Based on Constraint Satisfaction Techniques. ILP 2004: 234-251 | |
32 | Einoshin Suzuki: Undirected Exception Rule Discovery as Local Pattern Detection. Local Pattern Detection 2004: 207-216 | |
2003 | ||
31 | Ning Zhong, Zbigniew W. Ras, Shusaku Tsumoto, Einoshin Suzuki: Foundations of Intelligent Systems, 14th International Symposium, ISMIS 2003, Maebashi City, Japan, October 28-31, 2003, Proceedings Springer 2003 | |
30 | Yuu Yamada, Einoshin Suzuki, Hideto Yokoi, Katsuhiko Takabayashi: Experimental Evaluation of Time-Series Decision Tree. Active Mining 2003: 190-209 | |
29 | Jérôme Maloberti, Einoshin Suzuki: Improving Efficiency of Frequent Query Discovery by Eliminating Non-relevant Candidates. Discovery Science 2003: 220-232 | |
28 | Einoshin Suzuki, Takeshi Watanabe, Hideto Yokoi, Katsuhiko Takabayashi: Detecting Interesting Exceptions from Medical Test Data with Visual Summarization. ICDM 2003: 315-322 | |
27 | Yuu Yamada, Einoshin Suzuki, Hideto Yokoi, Katsuhiko Takabayashi: Decision-tree Induction from Time-series Data Based on a Standard-example Split Test. ICML 2003: 840-847 | |
26 | Masaki Narahashi, Einoshin Suzuki: Detecting Hostile Accesses through Incremental Subspace Clustering. Web Intelligence 2003: 337-343 | |
2002 | ||
25 | Masaki Narahashi, Einoshin Suzuki: Subspace Clustering Based on Compressibility. Discovery Science 2002: 435-440 | |
24 | Fumio Takechi, Einoshin Suzuki: Finding an Optimal Gain-Ratio Subset-Split Test for a Set-Valued Attribute in Decision Tree Induction. ICML 2002: 618-625 | |
23 | Shutaro Inatani, Einoshin Suzuki: Data Squashing for Speeding Up Boosting-Based Outlier Detection. ISMIS 2002: 601-612 | |
22 | Yuta Choki, Einoshin Suzuki: Iterative Data Squashing for Boosting Based on a Distribution-Sensitive Distance. PKDD 2002: 86-98 | |
21 | Einoshin Suzuki: In Pursuit of Interesting Patterns with Undirected Discovery of Exception Rules. Progress in Discovery Science 2002: 504-517 | |
20 | Einoshin Suzuki: Undirected Discovery of Interesting Exception Rules. IJPRAI 16(8): 1065-1086 (2002) | |
2001 | ||
19 | Einoshin Suzuki: Worst-Case Analysis of Rule Discovery. Discovery Science 2001: 365-377 | |
18 | Einoshin Suzuki, Masafumi Gotoh, Yuta Choki: Bloomy Decision Tree for Multi-objective Classification. PKDD 2001: 436-447 | |
2000 | ||
17 | Einoshin Suzuki: Issues in Organizing a Successful Knowledge Discovery Contest. Discovery Science 2000: 282-284 | |
16 | Einoshin Suzuki, Shusaku Tsumoto: Evaluating Hypothesis-Driven Exception-Rule Discovery with Medical Data Sets. PAKDD 2000: 208-211 | |
15 | Farhad Hussain, Huan Liu, Einoshin Suzuki, Hongjun Lu: Exception Rule Mining with a Relative Interestingness Measure. PAKDD 2000: 86-97 | |
14 | Einoshin Suzuki, Jan M. Zytkow: Unified Algorithm for Undirected Discovery of Execption Rules. PKDD 2000: 169-180 | |
13 | David Ramamonjisoa, Einoshin Suzuki, Issam A. Hamid: Research Topics Discovery from WWW by Keywords Association Rules. Rough Sets and Current Trends in Computing 2000: 412-419 | |
1999 | ||
12 | Einoshin Suzuki: Scheduled Discovery of Exception Rules. Discovery Science 1999: 184-195 | |
11 | Shinsuke Sugaya, Einoshin Suzuki: Normal Form Transformation for Object Recognition Based on Support Vector Machines. Discovery Science 1999: 306-315 | |
10 | Einoshin Suzuki, Toru Ohno: Prediction Rule Discovery Based on Dynamic Bias Selection. PAKDD 1999: 504-508 | |
9 | Shinsuke Sugaya, Einoshin Suzuki, Shusaku Tsumoto: Support Vector Machines for Knowledge Discovery. PKDD 1999: 561-567 | |
8 | Einoshin Suzuki, Hiroki Ishihara: Visualizing Discovered Rule Sets with Visual Graphs Based on Compressed Entropy Density. RSFDGrC 1999: 414-422 | |
1998 | ||
7 | Einoshin Suzuki: Simultaneous Reliability Evaluation of Generality and Accuracy for Rule Discovery in Databases. KDD 1998: 339-343 | |
6 | Einoshin Suzuki, Yves Kodratoff: Discovery of Surprising Exception Rules Based on Intensity of Implication. PKDD 1998: 10-18 | |
1997 | ||
5 | Einoshin Suzuki: Autonomous Discovery of Reliable Exception Rules. KDD 1997: 259-262 | |
1996 | ||
4 | Einoshin Suzuki, Masamichi Shimura: Exceptional Knowledge Discovery in Databases Based on Information Theory. KDD 1996: 275-278 | |
1994 | ||
3 | Pierre Morizet-Mahoudeaux, Einoshin Suzuki, Setsuo Ohsuga: Knowledge-Based Handling of Design Expertise. ICDE 1994: 368-374 | |
1993 | ||
2 | Einoshin Suzuki, Tatsuya Akutsu, Setsuo Ohsuga: Knowledge-based system for computer-aided drug design. Knowl.-Based Syst. 6(2): 114-126 (1993) | |
1991 | ||
1 | Tatsuya Akutsu, Einoshin Suzuki, Setsuo Ohsuga: Logic-based approach to expert systems in chemistry. Knowl.-Based Syst. 4(2): 103-116 (1991) |