- CSB - MetaPress - Google - Bing - Yahoo
 | 2013 | |
|---|---|---|
| c102 |      | Kazumi Saito, Masahiro Kimura, Kouzou Ohara, Hiroshi Motoda: Which Targets to Contact First to Maximize Influence over Social Network. SBP 2013: 359-367 |
| e6 | | Jian Pei, Vincent S. Tseng, Longbing Cao, Hiroshi Motoda, Guandong Xu (Eds.): Advances in Knowledge Discovery and Data Mining, 17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013, Proceedings, Part I. Lecture Notes in Computer Science 7818, Springer 2013, isbn 978-3-642-37452-4 |
| e5 | | Jian Pei, Vincent S. Tseng, Longbing Cao, Hiroshi Motoda, Guandong Xu (Eds.): Advances in Knowledge Discovery and Data Mining, 17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013, Proceedings, Part II. Lecture Notes in Computer Science 7819, Springer 2013, isbn 978-3-642-37455-5 |
| 2012 | ||
| j45 | | Kazumi Saito, Masahiro Kimura, Kouzou Ohara, Hiroshi Motoda: Efficient discovery of influential nodes for SIS models in social networks. Knowl. Inf. Syst. 30(3): 613-635 (2012) |
| c101 | | Kazumi Saito, Kouzou Ohara, Masahiro Kimura, Hiroshi Motoda: Burst Detection in a Sequence of Tweets Based on Information Diffusion Model. Discovery Science 2012: 239-253 |
| c100 | | Shoko Kato, Akihiro Koide, Takayasu Fushimi, Kazumi Saito, Hiroshi Motoda: Network Analysis of Three Twitter Functions: Favorite, Follow and Mention. PKAW 2012: 298-312 |
| c99 | | Masahiro Kimura, Kazumi Saito, Kouzou Ohara, Hiroshi Motoda: Opinion Formation by Voter Model with Temporal Decay Dynamics. ECML/PKDD (2) 2012: 565-580 |
| c98 | | Kouzou Ohara, Kazumi Saito, Masahiro Kimura, Hiroshi Motoda: Effect of In/Out-Degree Correlation on Influence Degree of Two Contrasting Information Diffusion Models. SBP 2012: 131-138 |
| c97 | | Kazumi Saito, Masahiro Kimura, Kouzou Ohara, Hiroshi Motoda: Graph embedding on spheres and its application to visualization of information diffusion data. WWW (Companion Volume) 2012: 1137-1144 |
| i2 | | Kazumi Saito, Masahiro Kimura, Kouzou Ohara, Hiroshi Motoda: Learning Asynchronous-Time Information Diffusion Models and its Application to Behavioral Data Analysis over Social Networks. CoRR abs/1204.4528 (2012) |
| 2011 | ||
| j44 | | Masahiro Kimura, Kazumi Saito, Kouzou Ohara, Hiroshi Motoda: Learning information diffusion model in a social network for predicting influence of nodes. Intell. Data Anal. 15(4): 633-652 (2011) |
| j43 | | Yuki Yamagishi, Kazumi Saito, Kouzou Ohara, Masahiro Kimura, Hiroshi Motoda: Learning Attribute-weighted Voter Model over Social Networks. Journal of Machine Learning Research - Proceedings Track 20: 263-280 (2011) |
| j42 | | Akihiro Koide, Kazumi Saito, Kouzou Ohara, Masahiro Kimura, Hiroshi Motoda: Estimating Diffusion Probability Changes for AsIC-SIS Model. Journal of Machine Learning Research - Proceedings Track 20: 297-313 (2011) |
| c96 | | Takayasu Fushimi, Yamato Kubota, Kazumi Saito, Masahiro Kimura, Kouzou Ohara, Hiroshi Motoda: Speeding Up Bipartite Graph Visualization Method. Australasian Conference on Artificial Intelligence 2011: 697-706 |
| c95 | | Masahiro Kimura, Kazumi Saito, Kouzou Ohara, Hiroshi Motoda: Detecting Anti-majority Opinionists Using Value-Weighted Mixture Voter Model. Discovery Science 2011: 150-164 |
| c94 | | Kazumi Saito, Kouzou Ohara, Yuki Yamagishi, Masahiro Kimura, Hiroshi Motoda: Learning Diffusion Probability Based on Node Attributes in Social Networks. ISMIS 2011: 153-162 |
| c93 | | Kazumi Saito, Masahiro Kimura, Kouzou Ohara, Hiroshi Motoda: Detecting Changes in Opinion Value Distribution for Voter Model. SBP 2011: 89-96 |
| c92 | | Hiroshi Motoda: Learning Information Diffusion Models from Observation and Its Application to Behavior Analysis. SocInfo 2011: 6 |
| i1 | | Kouzou Ohara, Kazumi Saito, Masahiro Kimura, Hiroshi Motoda: Efficient Detection of Hot Span in Information Diffusion from Observation. CoRR abs/1110.2659 (2011) |
| 2010 | ||
| j41 | | Masahiro Kimura, Kazumi Saito, Ryohei Nakano, Hiroshi Motoda: Extracting influential nodes on a social network for information diffusion. Data Min. Knowl. Discov. 20(1): 70-97 (2010) |
| j40 | | |
| j39 | | Huan Liu, Hiroshi Motoda, Rudy Setiono, Zheng Zhao: Preface. Journal of Machine Learning Research - Proceedings Track 10: 1-3 (2010) |
| j38 | | Huan Liu, Hiroshi Motoda, Rudy Setiono, Zheng Zhao: Feature Selection: An Ever Evolving Frontier in Data Mining. Journal of Machine Learning Research - Proceedings Track 10: 4-13 (2010) |
| j37 | | Kazumi Saito, Masahiro Kimura, Kouzou Ohara, Hiroshi Motoda: Generative Models of Information Diffusion with Asynchronous Timedelay. Journal of Machine Learning Research - Proceedings Track 13: 193-208 (2010) |
| c91 | | Masahiro Kimura, Kazumi Saito, Kouzou Ohara, Hiroshi Motoda: Learning to Predict Opinion Share in Social Networks. AAAI 2010 |
| c90 | | Kazumi Saito, Masahiro Kimura, Kouzou Ohara, Hiroshi Motoda: Discovery of Super-Mediators of Information Diffusion in Social Networks. Discovery Science 2010: 144-158 |
| c89 | | Takayasu Fushimi, Kazumi Saito, Masahiro Kimura, Hiroshi Motoda, Kouzou Ohara: Finding Relation between PageRank and Voter Model. PKAW 2010: 208-222 |
| c88 | | Yuya Yoshikawa, Kazumi Saito, Hiroshi Motoda, Kouzou Ohara, Masahiro Kimura: Acquiring Expected Influence Curve from Single Diffusion Sequence. PKAW 2010: 273-287 |
| c87 | | Kazumi Saito, Masahiro Kimura, Kouzou Ohara, Hiroshi Motoda: Selecting Information Diffusion Models over Social Networks for Behavioral Analysis. ECML/PKDD (3) 2010: 180-195 |
| c86 | | Kazumi Saito, Masahiro Kimura, Kouzou Ohara, Hiroshi Motoda: Efficient Estimation of Cumulative Influence for Multiple Activation Information Diffusion Model with Continuous Time Delay. PRICAI 2010: 244-255 |
| c85 | | Kazumi Saito, Masahiro Kimura, Kouzou Ohara, Hiroshi Motoda: Behavioral Analyses of Information Diffusion Models by Observed Data of Social Network. SBP 2010: 149-158 |
| 2009 | ||
| j36 | | |
| j35 | | Masahiro Kimura, Kazumi Saito, Hiroshi Motoda: Blocking links to minimize contamination spread in a social network. TKDD 3(2) (2009) |
| c84 | | Kazumi Saito, Masahiro Kimura, Kouzou Ohara, Hiroshi Motoda: Learning Continuous-Time Information Diffusion Model for Social Behavioral Data Analysis. ACML 2009: 322-337 |
| c83 | | Kazumi Saito, Masahiro Kimura, Hiroshi Motoda: Discovering Influential Nodes for SIS Models in Social Networks. Discovery Science 2009: 302-316 |
| c82 | | Masahiro Kimura, Kazumi Saito, Hiroshi Motoda: Efficient Estimation of Influence Functions for SIS Model on Social Networks. IJCAI 2009: 2046-2051 |
| 2008 | ||
| j34 | | Xindong Wu, Vipin Kumar, J. Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey J. McLachlan, Angus F. M. Ng, Bing Liu, Philip S. Yu, Zhi-Hua Zhou, Michael Steinbach, David J. Hand, Dan Steinberg: Top 10 algorithms in data mining. Knowl. Inf. Syst. 14(1): 1-37 (2008) |
| j33 | | Alexandre Termier, Marie-Christine Rousset, Michèle Sebag, Kouzou Ohara, Takashi Washio, Hiroshi Motoda: DryadeParent, An Efficient and Robust Closed Attribute Tree Mining Algorithm. IEEE Trans. Knowl. Data Eng. 20(3): 300-320 (2008) |
| c81 | | Masahiro Kimura, Kazumi Saito, Hiroshi Motoda: Minimizing the Spread of Contamination by Blocking Links in a Network. AAAI 2008: 1175-1180 |
| c80 | | Masahiro Kimura, Kazumasa Yamakawa, Kazumi Saito, Hiroshi Motoda: Community analysis of influential nodes for information diffusion on a social network. IJCNN 2008: 1358-1363 |
| c79 | | Kouzou Ohara, Masahiro Hara, Kiyoto Takabayashi, Hiroshi Motoda, Takashi Washio: Pruning Strategies Based on the Upper Bound of Information Gain for Discriminative Subgraph Mining. PKAW 2008: 50-60 |
| c78 | | Takayasu Fushimi, Takashi Kawazoe, Kazumi Saito, Masahiro Kimura, Hiroshi Motoda: What Does an Information Diffusion Model Tell about Social Network Structure?. PKAW 2008: 122-136 |
| c77 | | Kazumi Saito, Masahiro Kimura, Hiroshi Motoda: Effective Visualization of Information Diffusion Process over Complex Networks. ECML/PKDD (2) 2008: 326-341 |
| c76 | | Masahiro Kimura, Kazumi Saito, Hiroshi Motoda: Solving the Contamination Minimization Problem on Networks for the Linear Threshold Model. PRICAI 2008: 977-984 |
| 2007 | ||
| j32 | | Takashi Washio, Koutarou Nakanishi, Hiroshi Motoda: A Classification Method Based on Subspace Clustering and Association Rules. New Generation Comput. 25(3): 235-245 (2007) |
| c75 | | Takashi Washio, Hiroshi Motoda: Communicability Criteria of Law Equations Discovery. Computational Discovery of Scientific Knowledge 2007: 98-119 |
| c74 | | Hiroshi Motoda: Pattern Discovery from Graph-Structured Data - A Data Mining Perspective. IEA/AIE 2007: 12-22 |
| c73 | | Yang Sok Kim, Byeong Ho Kang, Paul Compton, Hiroshi Motoda: Search engine retrieval of changing information. WWW 2007: 1195-1196 |
| 2006 | ||
| j31 | | Toshiko Wakaki, Hiroyuki Itakura, Masaki Tamura, Hiroshi Motoda, Takashi Washio: A study on rough set-aided feature selection for automatic web-page classification. Web Intelligence and Agent Systems 4(4): 431-441 (2006) |
| c72 | | Hiroshi Motoda: What Can We Do with Graph-Structured Data? - A Data Mining Perspective. Australian Conference on Artificial Intelligence 2006: 1-2 |
| c71 | | Kenta Fukata, Takashi Washio, Hiroshi Motoda: A Method to Search ARX Model Orders and Its Application to Sales Dynamics Analysis. ICDM Workshops 2006: 590-595 |
| c70 | | Takashi Washio, Yasuo Shinnou, Katsutoshi Yada, Hiroshi Motoda, Takashi Okada: Analysis on a Relation Between Enterprise Profit and Financial State by Using Data Mining Techniques. JSAI 2006: 305-316 |
| c69 | | Phu Chien Nguyen, Kouzou Ohara, Akira Mogi, Hiroshi Motoda, Takashi Washio: Constructing Decision Trees for Graph-Structured Data by Chunkingless Graph-Based Induction. PAKDD 2006: 390-399 |
| c68 | | Kiyoto Takabayashi, Phu Chien Nguyen, Kouzou Ohara, Hiroshi Motoda, Takashi Washio: Extracting Discriminative Patterns from Graph Structured Data Using Constrained Search. PKAW 2006: 64-74 |
| 2005 | ||
| j30 | | Akihiro Inokuchi, Takashi Washio, Hiroshi Motoda: A General Framework for Mining Frequent Subgraphs from Labeled Graphs. Fundam. Inform. 66(1-2): 53-82 (2005) |
| j29 | | Warodom Geamsakul, Tetsuya Yoshida, Kouzou Ohara, Hiroshi Motoda, Hideto Yokoi, Katsuhiko Takabayashi: Constructing a Decision Tree for Graph-Structured Data and its Applications. Fundam. Inform. 66(1-2): 131-160 (2005) |
| j28 | | Takashi Washio, Hiroshi Motoda, Yuji Niwa: Enhancing the plausibility of law equation discovery through cross check among multiple scale-type-based models. J. Exp. Theor. Artif. Intell. 17(1-2): 129-143 (2005) |
| j27 | | Fuminori Adachi, Takashi Washio, Atsushi Fujimoto, Hiroshi Motoda, Hidemitsu Hanafusa: Multi-structure Information Retrieval Method Based on Transformation Invariance. New Generation Comput. 23(4): 291-313 (2005) |
| c67 | | Tetsuya Yoshida, Ryosuke Shoda, Hiroshi Motoda: Graph Clustering Based on Structural Similarity of Fragments. Federation over the Web 2005: 97-114 |
| c66 | | Takashi Washio, Fuminori Adachi, Hiroshi Motoda: SCALETRACK: A System to Discover Dynamic Law Equations Containing Hidden States and Chaos. Discovery Science 2005: 253-266 |
| c65 | | Alexandre Termier, Marie-Christine Rousset, Michèle Sebag, Kouzou Ohara, Takashi Washio, Hiroshi Motoda: Efficient Mining of High Branching Factor Attribute Trees. ICDM 2005: 785-788 |
| c64 | | Takashi Washio, Yuki Mitsunaga, Hiroshi Motoda: Mining Quantitative Frequent Itemsets Using Adaptive Density-Based Subspace Clustering. ICDM 2005: 793-796 |
| c63 | | Takashi Washio, Fuminori Adachi, Hiroshi Motoda: Discovering Time Differential Law Equations Containing Hidden State Variables and Chaotic Dynamics. IJCAI 2005: 1642-1644 |
| c62 | | Takashi Washio, Koutarou Nakanishi, Hiroshi Motoda, Takashi Okada: Mutagenicity Risk Analysis by Using Class Association Rules. JSAI Workshops 2005: 436-445 |
| c61 | | 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 |
| c60 | | Takashi Washio, Koutarou Nakanishi, Hiroshi Motoda: Deriving Class Association Rules Based on Levelwise Subspace Clustering. PKDD 2005: 692-700 |
| c59 | | Takashi Washio, Atsushi Fujimoto, Hiroshi Motoda: A Framework of Numerical Basket Analysis. SAINT Workshops 2005: 340-343 |
| c58 | | 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 |
| e4 |  | Shusaku Tsumoto, Takahira Yamaguchi, Masayuki Numao, Hiroshi Motoda (Eds.): Active Mining, Second International Workshop, AM 2003, Maebashi, Japan, October 28, 2003, Revised Selected Papers. Lecture Notes in Computer Science 3430, Springer 2005, isbn 3-540-26157-5 |
| e3 | | Achim G. Hoffmann, Hiroshi Motoda, Tobias Scheffer (Eds.): Discovery Science, 8th International Conference, DS 2005, Singapore, October 8-11, 2005, Proceedings. Lecture Notes in Computer Science 3735, Springer 2005, isbn 3-540-29230-6 |
| 2004 | ||
| j26 | | Huan Liu, Hiroshi Motoda, Lei Yu: A selective sampling approach to active feature selection. Artif. Intell. 159(1-2): 49-74 (2004) |
| j25 | | Tetsuya Yoshida, Takuya Wada, Hiroshi Motoda, Takashi Washio: Adaptive Ripple Down Rules method based on minimum description length principle. Intell. Data Anal. 8(3): 239-265 (2004) |
| j24 | | Kenichi Yoshida, Fuminori Adachi, Takashi Washio, Hiroshi Motoda, Teruaki Homma, Akihiro Nakashima, Hiromitsu Fujikawa, Katsuyuki Yamazaki: Density-Based Spam Detector. IEICE Transactions 87-D(12): 2678-2688 (2004) |
| j23 | | Nada Lavrac, Hiroshi Motoda, Tom Fawcett: Editorial: Data Mining Lessons Learned. Machine Learning 57(1-2): 5-11 (2004) |
| j22 | | Nada Lavrac, Hiroshi Motoda, Tom Fawcett, Robert Holte, Pat Langley, Pieter W. Adriaans: Introduction: Lessons Learned from Data Mining Applications and Collaborative Problem Solving. Machine Learning 57(1-2): 13-34 (2004) |
| c57 | | Kouzou Ohara, Yukio Onishi, Noboru Babaguchi, Hiroshi Motoda: Constructive Inductive Learning Based on Meta-attributes. Discovery Science 2004: 142-154 |
| c56 | | Warodom Geamsakul, Takashi Matsuda, Tetsuya Yoshida, Kouzou Ohara, Hiroshi Motoda, Takashi Washio, Hideto Yokoi, Katsuhiko Takabayashi: Analysis of Hepatitis Dataset by Decision Tree Based on Graph-Based Induction. JSAI Workshops 2004: 5-28 |
| c55 | | Masayuki Numao, Takahira Yamaguchi, Shusaku Tsumoto, Hiroshi Motoda: Workshop on Active Mining (AM-2004). JSAI Workshops 2004: 463 |
| c54 | | Kenichi Yoshida, Fuminori Adachi, Takashi Washio, Hiroshi Motoda, Teruaki Homma, Akihiro Nakashima, Hiromitsu Fujikawa, Katsuyuki Yamazaki: Density-based spam detector. KDD 2004: 486-493 |
| c53 | | Katsutoshi Yada, Hiroshi Motoda, Takashi Washio, Asuka Miyawaki: Consumer Behavior Analysis by Graph Mining Technique. KES 2004: 800-806 |
| c52 | | Amit Mandvikar, Huan Liu, Hiroshi Motoda: Compact Dual Ensembles for Active Learning. PAKDD 2004: 293-297 |
| c51 | | Phu Chien Nguyen, Takashi Washio, Kouzou Ohara, Hiroshi Motoda: Using a Hash-Based Method for Apriori-Based Graph Mining. PKDD 2004: 349-361 |
| 2003 | ||
| j21 | | Akihiro Inokuchi, Takashi Washio, Hiroshi Motoda: Complete Mining of Frequent Patterns from Graphs: Mining Graph Data. Machine Learning 50(3): 321-354 (2003) |
| j20 | | Takashi Washio, Hiroshi Motoda: State of the art of graph-based data mining. SIGKDD Explorations 5(1): 59-68 (2003) |
| j19 | | Setsuo Arikawa, Koichi Furukawa, Shinichi Morishita, Hiroshi Motoda: Preface. Theor. Comput. Sci. 292(2): 343-344 (2003) |
| c50 | | Shusaku Tsumoto, Takahira Yamaguchi, Masayuki Numao, Hiroshi Motoda: Active Mining Project: Overview. Active Mining 2003: 1-10 |
| c49 | | Warodom Geamsakul, Tetsuya Yoshida, Kouzou Ohara, Hiroshi Motoda, Takashi Washio, Hideto Yokoi, Katsuhiko Takabayashi: Extracting Diagnostic Knowledge from Hepatitis Dataset by Decision Tree Graph-Based Induction. Active Mining 2003: 126-151 |
| c48 | | Warodom Geamsakul, Takashi Matsuda, Tetsuya Yoshida, Hiroshi Motoda, Takashi Washio: Performance Evaluation of Decision Tree Graph-Based Induction. Discovery Science 2003: 128-140 |
| c47 | | Fuminori Adachi, Takashi Washio, Hiroshi Motoda, Atsushi Fujimoto, Hidemitsu Hanafusa: Development of Generic Search Method Based on Transformation Invariance. ISMIS 2003: 486-495 |
| c46 | | Warodom Geamsakul, Takashi Matsuda, Tetsuya Yoshida, Hiroshi Motoda, Takashi Washio: Classifier Construction by Graph-Based Induction for Graph-Structured Data. PAKDD 2003: 52-62 |
| c45 | | Huan Liu, Lei Yu, Manoranjan Dash, Hiroshi Motoda: Active Feature Selection Using Classes. PAKDD 2003: 474-485 |
| 2002 | ||
| j18 | | Takashi Matsuda, Hiroshi Motoda, Takashi Washio: Graph-based induction and its applications. Advanced Engineering Informatics 16(2): 135-143 (2002) |
| j17 | | Huan Liu, Hiroshi Motoda: On Issues of Instance Selection. Data Min. Knowl. Discov. 6(2): 115-130 (2002) |
| j16 | | Masahiro Terabe, Takashi Washio, Hiroshi Motoda, Osamu Katai, Tetsuo Sawaragi: Attribute Generation Based on Association Rules. Knowl. Inf. Syst. 4(3): 329-349 (2002) |
| c44 | | Takashi Matsuda, Hiroshi Motoda, Tetsuya Yoshida, Takashi Washio: Mining Patterns from Structured Data by Beam-Wise Graph-Based Induction. Discovery Science 2002: 422-429 |
| c43 | | Takashi Washio, Hiroshi Motoda: Toward the Discovery of First Principle Based Scientific Law Equations. Progress in Discovery Science 2002: 553-564 |
| c42 | | Tetsuya Yoshida, Hiroshi Motoda, Takashi Washio: Adaptive Ripple Down Rules Method based on Minimum Description Length Principle. ICDM 2002: 530-537 |
| c41 | | |
| c40 | | 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 |
| c39 | | Keisei Fujiwara, Tetsuya Yoshida, Hiroshi Motoda, Takashi Washio: Case Generation Method for Constructing an RDR Knowledge Base. PRICAI 2002: 228-237 |
| c38 | | Takashi Matsuda, Hiroshi Motoda, Tetsuya Yoshida, Takashi Washio: Knowledge Discovery from Structured Data by Beam-Wise Graph-Based Induction. PRICAI 2002: 255-264 |
| 2001 | ||
| j15 | | 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) |
| c37 | | Takashi Washio, Hiroshi Motoda, Yuji Niwa: Discovering Admissible Simultaneous Equation Models from Observed Data. ECML 2001: 539-551 |
| c36 | | Masahiro Terabe, Takashi Washio, Hiroshi Motoda: S3Bagging: Fast Classifier Induction Method with Subsampling and Bagging. IDA 2001: 177-186 |
| c35 | | Takayuki Ikeda, Takashi Washio, Hiroshi Motoda: Basket Analysis on Meningitis Data. JSAI Workshops 2001: 516-524 |
| c34 | | Takuya Wada, Hiroshi Motoda, Takashi Washio: Knowledge Acquisition from Both Human Expert and Data. PAKDD 2001: 550-561 |
| c33 | | Makoto Tsukada, Takashi Washio, Hiroshi Motoda: Automatic Web-Page Classification by Using Machine Learning Methods. Web Intelligence 2001: 303-313 |
| 2000 | ||
| j14 | | Hiroshi Motoda, Setsuo Arikawa: Special Feature on Discovery Science. New Generation Comput. 18(1): 13-16 (2000) |
| c32 | | 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 |
| c31 | | Takashi Washio, Hiroshi Motoda, Yuji Niwa: Enhancing the Plausibility of Law Equation Discovery. ICML 2000: 1127-1134 |
| c30 | | |
| c29 | | Takashi Matsuda, Tadashi Horiuchi, Hiroshi Motoda, Takashi Washio: Extension of Graph-Based Induction for General Graph Structured Data. PAKDD 2000: 420-431 |
| c28 | | Akihiro Inokuchi, Takashi Washio, Hiroshi Motoda: An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data. PKDD 2000: 13-23 |
| 1999 | ||
| c27 | | Manoranjan Dash, Huan Liu, Hiroshi Motoda: Feature Selection Using Consistency Measure. Discovery Science 1999: 319-320 |
| c26 | | Akihiro Inokuchi, Takashi Washio, Hiroshi Motoda: Derivation of the Topology Structure from Massive Graph Data. Discovery Science 1999: 330-332 |
| c25 | | 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 |
| c24 | | Takashi Washio, Hiroshi Motoda, Niwa Yuji: Discovering Admissible Model Equations from Observed Data Based on Scale-Types and Identity Constrains. IJCAI 1999: 772-779 |
| c23 | | Hiroshi Motoda: Computer Assisted Discovery of First Principle Equations from Numeric Data (Abstract). PAKDD 1999: 2 |
| c22 | | 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 |
| c21 | | Takuya Wada, Tadashi Horiuchi, Hiroshi Motoda, Takashi Washio: Characterization of Default Knowledge in Ripple Down Rules Method. PAKDD 1999: 284-295 |
| c20 | | Akihiro Inokuchi, Takashi Washio, Hiroshi Motoda, Kouhei Kumasawa, Naohide Arai: Basket Analysis for Graph Structured Data. PAKDD 1999: 420-431 |
| 1998 | ||
| j13 | | Hiroshi Motoda, Kenichi Yoshida: Machine Learning Techniques to Make Computers Easier to Use. Artif. Intell. 103(1-2): 295-321 (1998) |
| j12 | | Huan Liu, Hiroshi Motoda: Guest Editors' Introduction: Feature Transformation and Subset Selection. IEEE Intelligent Systems 13(2): 26-28 (1998) |
| j11 | | Hing-Yan Lee, Hongjun Lu, Hiroshi Motoda: Knowledge discovery and data mining. Knowl.-Based Syst. 10(7): 401-402 (1998) |
| j10 | | Takashi Washio, Hiroshi Motoda: Discovery of first-principle equations based on scale-type-based and data-driven reasoning. Knowl.-Based Syst. 10(7): 403-411 (1998) |
| c19 | | Takashi Washio, Hiroshi Motoda: Discovering Admissible Simultaneous Equations of Large Scale Systems. AAAI/IAAI 1998: 189-196 |
| c18 | | Takashi Washio, Hiroshi Motoda: Development of SDS2: Smart Discovery System for Simultaneous Equation Systems. Discovery Science 1998: 352-363 |
| c17 | | Huan Liu, Hiroshi Motoda, Manoranjan Dash: A Monotonic Measure for Optimal Feature Selection. ECML 1998: 101-106 |
| c16 | | Takashi Washio, Hiroshi Motoda: Mining Association Rules for Estimation and Prediction. PAKDD 1998: 417-419 |
| e2 | | Setsuo Arikawa, Hiroshi Motoda (Eds.): Discovery Science, First International Conference, DS '98, Fukuoka, Japan, December 14-16, 1998, Proceedings. Lecture Notes in Computer Science 1532, Springer 1998, isbn 3-540-65390-2 |
| e1 | | Hing-Yan Lee, Hiroshi Motoda (Eds.): PRICAI'98, Topics in Artificial Intelligence, 5th Pacific Rim International Conference on Artificial Intelligence, Singapore, November 22-27, 1998, Proceedings. Lecture Notes in Computer Science 1531, Springer 1998, isbn 3-540-65271-X |
| 1997 | ||
| j9 | | Byeong Ho Kang, Kenichi Yoshida, Hiroshi Motoda, Paul Compton: Help Desk System with Intelligent Interface. Applied Artificial Intelligence 11(7-8): 611-631 (1997) |
| c15 | | Takashi Washio, Hiroshi Motoda: Discovering Admissible Models of Complex Systems Based on Scale-Types and Idemtity Constraints. IJCAI (2) 1997: 810-819 |
| c14 | | Hiroshi Motoda, Kenichi Yoshida: Machine Learning Techniques to Make Computers Easier to Use. IJCAI 1997: 1622-1631 |
| 1996 | ||
| j8 | | Kenichi Yoshida, Hiroshi Motoda: Automated user modeling for intelligent interface. Int. J. Hum. Comput. Interaction 8(3): 237-258 (1996) |
| c13 | | |
| c12 | | Shingo Nishioka, Atsuo Kawaguchi, Hiroshi Motoda: Process Labeled Kernel Profiling: A New Facility to Profile System Activities. USENIX Annual Technical Conference 1996: 295-306 |
| 1995 | ||
| j7 | | Kenichi Yoshida, Hiroshi Motoda: CLIP: Concept Learning from Inference Patterns. Artif. Intell. 75(1): 63-92 (1995) |
| j6 | | Riichiro Mizoguchi, Hiroshi Motoda: Expert Systems Research in Japan. IEEE Expert 10(4): 14-23 (1995) |
| c11 | | Kenichi Yoshida, Hiroshi Motoda: Tables, Graphs and Logic for Induction. Machine Intelligence 15 1995: 298-311 |
| c10 | | Atsuo Kawaguchi, Shingo Nishioka, Hiroshi Motoda: A Flash-Memory Based File System. USENIX Winter 1995: 155-164 |
| 1994 | ||
| j5 | | Masaki Suwa, Hiroshi Motoda: PCLEARN: A Computer Model for Learning Perceptual Chunks. AI Commun. 7(2): 114-125 (1994) |
| j4 | | Kenichi Yoshida, Hiroshi Motoda, Nitin Indurkhya: Graph-based induction as a unified learning framework. Appl. Intell. 4(3): 297-316 (1994) |
| c9 | | N. Hari Narayanan, Masaki Suwa, Hiroshi Motoda: How Things Appear to Work: Predicting Behaviors from Device Diagrams. AAAI 1994: 1161-1167 |
| c8 | | Masaki Suwa, Hiroshi Motoda: Learning Perceptually Chunked Macro Operators. Machine Intelligence 13 1994: 419-440 |
| 1993 | ||
| c7 | | Makoto Iwayama, Nitin Indurkhya, Hiroshi Motoda: A New Algorithm for Automatic Configuration of Hidden Markov Models. ALT 1993: 237-250 |
| c6 | | Kenichi Yoshida, Hiroshi Motoda, Nitin Indurkhya: Unifying Learning Methods by Colored Digraphs. ALT 1993: 342-355 |
| c5 | | Masaki Suwa, Hiroshi Motoda: A Perceptual Criterion for Visually Controlling Learning. ALT 1993: 356-369 |
| c4 | | Masaki Suwa, Hiroshi Motoda: On dealing with dynamic utility of learned knowledge. Machine Intelligence 14 1993: 113- |
| 1991 | ||
| j3 | | Hiroshi Motoda, Riichiro Mizoguchi, John H. Boose, Brian R. Gaines: Knowledge Acquisition for Knowledge-Based Systems. IEEE Expert 6(4): 53-64 (1991) |
| j2 | | Atsuo Kawaguchi, Hiroshi Motoda, Riichiro Mizoguchi: Interview-Based Knowledge Acquisition Using Dynamic Analysis. IEEE Expert 6(5): 47-60 (1991) |
| 1990 | ||
| j1 | | Hiroshi Motoda: The Current Status of Expert System Development and Related Technologies in Japan. IEEE Expert 5(4): 3-11 (1990) |
| 1988 | ||
| c3 | | Akito Sakurai, Hiroshi Motoda: Proving Definite Clauses without Explicit Use of Inductions. LP 1988: 11-26 |
| 1984 | ||
| c2 | | Hiroshi Motoda, Naoyuki Yamada, Kenichi Yoshida: A Knowledge based System for Plant Diagnosis. FGCS 1984: 582-588 |
| 1983 | ||
| c1 | | Naoyuki Yamada, Hiroshi Motoda: A Diagnosis Method of Dynamic System Using the Knowledge on System Description. IJCAI 1983: 225-229 |
Colors in the list of coauthors
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