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Note: Links lead to the DBLP on the Web. Takashi Washio Phu Chien Nguyen , Kouzou Ohara , Hiroshi Motoda , Takashi Washio: Cl-GBI: A Novel Approach for Extracting Typical Patterns from Graph-Structured Data. PAKDD 2005 : 639-649 Kenichi Yoshida , Fuminori Adachi , Takashi Washio, Hiroshi Motoda , Teruaki Homma , Akihiro Nakashima , Hiromitsu Fujikawa , Katsuyuki Yamazaki : Memory Management of Density-Based Spam Detector. SAINT 2005 : 370-376 Takashi Washio, Atsushi Fujimoto , Hiroshi Motoda : A Framework of Numerical Basket Analysis. SAINT Workshops 2005 : 340-343 Kenichi Yoshida , Fuminori Adachi , Takashi Washio, Hiroshi Motoda , Teruaki Homma , Akihiro Nakashima , Hiromitsu Fujikawa , Katsuyuki Yamazaki : Density-based spam detector. KDD 2004 : 486-493 Katsutoshi Yada , Hiroshi Motoda , Takashi Washio, Asuka Miyawaki : Consumer Behavior Analysis by Graph Mining Technique. KES 2004 : 800-806 Phu Chien Nguyen , Takashi Washio, Kouzou Ohara , Hiroshi Motoda : Using a Hash-Based Method for Apriori-Based Graph Mining. PKDD 2004 : 349-361 Warodom Geamsakul , Takashi Matsuda , Tetsuya Yoshida , Hiroshi Motoda , Takashi Washio: Performance Evaluation of Decision Tree Graph-Based Induction. Discovery Science 2003 : 128-140 Fuminori Adachi , Takashi Washio, Hiroshi Motoda , Atsushi Fujimoto , Hidemitsu Hanafusa : Development of Generic Search Method Based on Transformation Invariance. ISMIS 2003 : 486-495 Warodom Geamsakul , Takashi Matsuda , Tetsuya Yoshida , Hiroshi Motoda , Takashi Washio: Classifier Construction by Graph-Based Induction for Graph-Structured Data. PAKDD 2003 : 52-62 Akihiro Inokuchi , Takashi Washio, Hiroshi Motoda : Complete Mining of Frequent Patterns from Graphs: Mining Graph Data. Machine Learning 50 (3): 321-354 (2003) Takashi Washio, Hiroshi Motoda : State of the art of graph-based data mining. SIGKDD Explorations 5 (1): 59-68 (2003) Takashi Matsuda , Hiroshi Motoda , Tetsuya Yoshida , Takashi Washio: Mining Patterns from Structured Data by Beam-Wise Graph-Based Induction. Discovery Science 2002 : 422-429 Tetsuya Yoshida , Hiroshi Motoda , Takashi Washio: Adaptive Ripple Down Rules Method based on Minimum Description Length Principle. ICDM 2002 : 530-537 Takuya Wada , Tetsuya Yoshida , Hiroshi Motoda , Takashi Washio: Extension of the RDR Method That Can Adapt to Environmental Changes and Acquire Knowledge from Both Experts and Data. PRICAI 2002 : 218-227 Keisei Fujiwara , Tetsuya Yoshida , Hiroshi Motoda , Takashi Washio: Case Generation Method for Constructing an RDR Knowledge Base. PRICAI 2002 : 228-237 Takashi Matsuda , Hiroshi Motoda , Tetsuya Yoshida , Takashi Washio: Knowledge Discovery from Structured Data by Beam-Wise Graph-Based Induction. PRICAI 2002 : 255-264 Hiroshi H. Hasegawa , Takashi Washio, Yukari Ishimiya : Inductive Thermodynamics from Time Series Data Analysis. Progress in Discovery Science 2002 : 384-394 Takashi Washio, Hiroshi Motoda : Toward the Discovery of First Principle Based Scientific Law Equations. Progress in Discovery Science 2002 : 553-564 Masahiro Terabe , Takashi Washio, Hiroshi Motoda , Osamu Katai , Tetsuo Sawaragi : Attribute Generation Based on Association Rules. Knowl. Inf. Syst. 4 (3): 329-349 (2002) Takao Terano , Toyoaki Nishida , Akira Namatame , Shusaku Tsumoto , Yukio Ohsawa , Takashi Washio: New Frontiers in Artificial Intelligence, Joint JSAI 2001 Workshop Post-Proceedings Springer 2001 Takashi Washio, Hiroshi Motoda , Yuji Niwa : Discovering Admissible Simultaneous Equation Models from Observed Data. ECML 2001 : 539-551 Masahiro Terabe , Takashi Washio, Hiroshi Motoda : S 3 Bagging: Fast Classifier Induction Method with Subsampling and Bagging. IDA 2001 : 177-186 Takashi Washio: JSAI KDD Challenge 2001: JKDD01. JSAI Workshops 2001 : 499 Takayuki Ikeda , Takashi Washio, Hiroshi Motoda : Basket Analysis on Meningitis Data. JSAI Workshops 2001 : 516-524 Takuya Wada , Hiroshi Motoda , Takashi Washio: Knowledge Acquisition from Both Human Expert and Data. PAKDD 2001 : 550-561 Makoto Tsukada , Takashi Washio, Hiroshi Motoda : Automatic Web-Page Classification by Using Machine Learning Methods. Web Intelligence 2001 : 303-313 Takuya Wada , Tadashi Horiuchi , Hiroshi Motoda , Takashi Washio: A Description Length-Based Decision Criterion for Default Knowledge in the Ripple Down Rules Method. Knowl. Inf. Syst. 3 (2): 146-167 (2001) Hiroshi H. Hasegawa , Takashi Washio, Yukari Ishimiya , Takeshi Saito : Nonequilibrium Thermodynamics from Time Series Data Analysis. Discovery Science 2000 : 304-305 Takashi Matsuda , Tadashi Horiuchi , Hiroshi Motoda , Takashi Washio: Graph-Based Induction for General Graph Structured Data and Its Application to Chemical Compound Data. Discovery Science 2000 : 99-111 Takashi Washio, Hiroshi Motoda , Yuji Niwa : Enhancing the Plausibility of Law Equation Discovery. ICML 2000 : 1127-1134 Takashi Matsuda , Tadashi Horiuchi , Hiroshi Motoda , Takashi Washio: Extension of Graph-Based Induction for General Graph Structured Data. PAKDD 2000 : 420-431 Akihiro Inokuchi , Takashi Washio, Hiroshi Motoda : An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data. PKDD 2000 : 13-23 Hiroshi H. Hasegawa , Takashi Washio, Yukari Ishimiya : ``Thermodynamics'' from Time Series Data Analysis. Discovery Science 1999 : 326-327 Akihiro Inokuchi , Takashi Washio, Hiroshi Motoda : Derivation of the Topology Structure from Massive Graph Data. Discovery Science 1999 : 330-332 Takashi Matsuda , Tadashi Horiuchi , Hiroshi Motoda , Takashi Washio, Kohei Kumazawa , Naohide Arai : Graph-Based Induction for General Graph Structured Data. Discovery Science 1999 : 340-342 Takashi Washio, Hiroshi Motoda , Niwa Yuji : Discovering Admissible Model Equations from Observed Data Based on Scale-Types and Identity Constrains. IJCAI 1999 : 772-779 Masahiro Terabe , Osamu Katai , Tetsuo Sawaragi , Takashi Washio, Hiroshi Motoda : A Data Pre-processing Method Using Association Rules of Attributes for Improving Decision Tree. PAKDD 1999 : 143-147 Takuya Wada , Tadashi Horiuchi , Hiroshi Motoda , Takashi Washio: Characterization of Default Knowledge in Ripple Down Rules Method. PAKDD 1999 : 284-295 Akihiro Inokuchi , Takashi Washio, Hiroshi Motoda , Kouhei Kumasawa , Naohide Arai : Basket Analysis for Graph Structured Data. PAKDD 1999 : 420-431 Takashi Washio, Hiroshi Motoda : Discovering Admissible Simultaneous Equations of Large Scale Systems. AAAI/IAAI 1998 : 189-196 Takashi Washio, Hiroshi Motoda : Development of SDS2: Smart Discovery System for Simultaneous Equation Systems. Discovery Science 1998 : 352-363 Takashi Washio, Hiroshi Motoda : Mining Association Rules for Estimation and Prediction. PAKDD 1998 : 417-419 Takashi Washio, Hiroshi Motoda : Discovering Admissible Models of Complex Systems Based on Scale-Types and Idemtity Constraints. IJCAI (2) 1997 : 810-819 Takashi Washio, M. Sakuma , M. Kitamura : A New Approach to Quantitative and Credible Diagnosis for Multiple Faults of Components and Sensors. Artif. Intell. 91 (1): 103-130 (1997) Masahiro Terabe , Takashi Washio, Osamu Katai , Tetsuo Sawaragi : A Study of Organizational Learning in Multi-Agent Sytems. ECAI Workshop LDAIS / ICMAS Workshop LIOME 1996 : 168-179 Takashi Washio, Hiroshi Motoda : A History-Oriented Envisioning Method. PRICAI 1996 : 312-323 Rolf Hempel , Robin Calkin , Reinhold Hess , Wolfgang Joppich , C. W. Oosterlee , Hubert Ritzdorf , P. Wypior , Wolfgang Ziegler , N. Koike , Takashi Washio, U. Keller : Real Applications on the New Parallel System NEC Cenju-3. Parallel Computing 22 (1): 131-148 (1996) 1 [ 40 ] [ 44 ] [ 46 ] 2 [ 9 ] [ 13 ] 3 [ 1 ] 4 [ 44 ] [ 46 ] 5 [ 40 ] [ 45 ] 6 [ 33 ] 7 [ 39 ] [ 41 ] 8 [ 40 ] 9 [ 15 ] [ 20 ] [ 31 ] 10 [ 1 ] 11 [ 1 ] 12 [ 44 ] [ 46 ] 13 [ 10 ] [ 13 ] [ 17 ] [ 19 ] [ 21 ] 14 [ 24 ] 15 [ 9 ] [ 14 ] [ 16 ] [ 38 ] 16 [ 15 ] [ 20 ] [ 31 ] 17 [ 1 ] 18 [ 3 ] [ 11 ] [ 29 ] 19 [ 1 ] 20 [ 4 ] 21 [ 1 ] 22 [ 9 ] 23 [ 13 ] 24 [ 13 ] [ 17 ] [ 19 ] [ 32 ] [ 36 ] [ 39 ] [ 41 ] 25 [ 43 ] 26 [ 2 ] [ 5 ] [ 6 ] [ 7 ] [ 8 ] [ 9 ] [ 10 ] [ 11 ] [ 12 ] [ 13 ] [ 14 ] [ 16 ] [ 17 ] [ 18 ] [ 19 ] [ 21 ] [ 22 ] [ 23 ] [ 24 ] [ 26 ] [ 27 ] [ 29 ] [ 30 ] [ 32 ] [ 33 ] [ 34 ] [ 35 ] [ 36 ] [ 37 ] [ 38 ] [ 39 ] [ 40 ] [ 41 ] [ 42 ] [ 43 ] [ 44 ] [ 45 ] [ 46 ] [ 47 ] 27 [ 44 ] [ 46 ] 28 [ 28 ] 29 [ 42 ] [ 47 ] 30 [ 28 ] 31 [ 18 ] [ 27 ] 32 [ 42 ] [ 47 ] 33 [ 28 ] 34 [ 1 ] 35 [ 1 ] 36 [ 20 ] 37 [ 4 ] 38 [ 3 ] [ 11 ] [ 29 ] 39 [ 3 ] [ 11 ] [ 26 ] [ 29 ] 40 [ 28 ] 41 [ 22 ] 42 [ 28 ] 43 [ 10 ] [ 21 ] [ 23 ] [ 34 ] 44 [ 1 ] 45 [ 43 ] 46 [ 44 ] [ 46 ] 47 [ 44 ] [ 46 ] 48 [ 32 ] [ 33 ] [ 34 ] [ 35 ] [ 36 ] [ 39 ] [ 41 ] 49 [ 12 ] 50 [ 1 ]  ©2005 Association for Computing Machinery |
