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Hiroshi Motoda: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Kenichi Yoshida, Hiroshi Motoda, Nitin Indurkhya
    Unifying Learning Methods by Colored Digraphs. [Citation Graph (1, 0)][DBLP]
    ALT, 1993, pp:342-355 [Conf]
  2. N. Hari Narayanan, Masaki Suwa, Hiroshi Motoda
    How Things Appear to Work: Predicting Behaviors from Device Diagrams. [Citation Graph (0, 0)][DBLP]
    AAAI, 1994, pp:1161-1167 [Conf]
  3. Takashi Washio, Hiroshi Motoda
    Discovering Admissible Simultaneous Equations of Large Scale Systems. [Citation Graph (0, 0)][DBLP]
    AAAI/IAAI, 1998, pp:189-196 [Conf]
  4. Makoto Iwayama, Nitin Indurkhya, Hiroshi Motoda
    A New Algorithm for Automatic Configuration of Hidden Markov Models. [Citation Graph (0, 0)][DBLP]
    ALT, 1993, pp:237-250 [Conf]
  5. Masaki Suwa, Hiroshi Motoda
    A Perceptual Criterion for Visually Controlling Learning. [Citation Graph (0, 0)][DBLP]
    ALT, 1993, pp:356-369 [Conf]
  6. 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. [Citation Graph (0, 0)][DBLP]
    Active Mining, 2003, pp:126-151 [Conf]
  7. Shusaku Tsumoto, Takahira Yamaguchi, Masayuki Numao, Hiroshi Motoda
    Active Mining Project: Overview. [Citation Graph (0, 0)][DBLP]
    Active Mining, 2003, pp:1-10 [Conf]
  8. Hiroshi Motoda
    What Can We Do with Graph-Structured Data? - A Data Mining Perspective. [Citation Graph (0, 0)][DBLP]
    Australian Conference on Artificial Intelligence, 2006, pp:1-2 [Conf]
  9. Warodom Geamsakul, Takashi Matsuda, Tetsuya Yoshida, Hiroshi Motoda, Takashi Washio
    Performance Evaluation of Decision Tree Graph-Based Induction. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2003, pp:128-140 [Conf]
  10. Manoranjan Dash, Huan Liu, Hiroshi Motoda
    Feature Selection Using Consistency Measure. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 1999, pp:319-320 [Conf]
  11. Akihiro Inokuchi, Takashi Washio, Hiroshi Motoda
    Derivation of the Topology Structure from Massive Graph Data. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 1999, pp:330-332 [Conf]
  12. Takashi Matsuda, Tadashi Horiuchi, Hiroshi Motoda, Takashi Washio
    Graph-Based Induction for General Graph Structured Data and Its Application to Chemical Compound Data. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2000, pp:99-111 [Conf]
  13. Takashi Matsuda, Tadashi Horiuchi, Hiroshi Motoda, Takashi Washio, Kohei Kumazawa, Naohide Arai
    Graph-Based Induction for General Graph Structured Data. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 1999, pp:340-342 [Conf]
  14. Takashi Matsuda, Hiroshi Motoda, Tetsuya Yoshida, Takashi Washio
    Mining Patterns from Structured Data by Beam-Wise Graph-Based Induction. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2002, pp:422-429 [Conf]
  15. Kouzou Ohara, Yukio Onishi, Noboru Babaguchi, Hiroshi Motoda
    Constructive Inductive Learning Based on Meta-attributes. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2004, pp:142-154 [Conf]
  16. Takashi Washio, Fuminori Adachi, Hiroshi Motoda
    SCALETRACK: A System to Discover Dynamic Law Equations Containing Hidden States and Chaos. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2005, pp:253-266 [Conf]
  17. Takashi Washio, Hiroshi Motoda
    Development of SDS2: Smart Discovery System for Simultaneous Equation Systems. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 1998, pp:352-363 [Conf]
  18. Takashi Washio, Hiroshi Motoda
    Toward the Discovery of First Principle Based Scientific Law Equations. [Citation Graph (0, 0)][DBLP]
    Progress in Discovery Science, 2002, pp:553-564 [Conf]
  19. Huan Liu, Hiroshi Motoda, Manoranjan Dash
    A Monotonic Measure for Optimal Feature Selection. [Citation Graph (0, 0)][DBLP]
    ECML, 1998, pp:101-106 [Conf]
  20. Takashi Washio, Hiroshi Motoda, Yuji Niwa
    Discovering Admissible Simultaneous Equation Models from Observed Data. [Citation Graph (0, 0)][DBLP]
    ECML, 2001, pp:539-551 [Conf]
  21. Hiroshi Motoda, Naoyuki Yamada, Kenichi Yoshida
    A Knowledge based System for Plant Diagnosis. [Citation Graph (0, 0)][DBLP]
    FGCS, 1984, pp:582-588 [Conf]
  22. Alexandre Termier, Marie-Christine Rousset, Michèle Sebag, Kouzou Ohara, Takashi Washio, Hiroshi Motoda
    Efficient Mining of High Branching Factor Attribute Trees. [Citation Graph (0, 0)][DBLP]
    ICDM, 2005, pp:785-788 [Conf]
  23. Takashi Washio, Yuki Mitsunaga, Hiroshi Motoda
    Mining Quantitative Frequent Itemsets Using Adaptive Density-Based Subspace Clustering. [Citation Graph (0, 0)][DBLP]
    ICDM, 2005, pp:793-796 [Conf]
  24. Tetsuya Yoshida, Hiroshi Motoda, Takashi Washio
    Adaptive Ripple Down Rules Method based on Minimum Description Length Principle. [Citation Graph (0, 0)][DBLP]
    ICDM, 2002, pp:530-537 [Conf]
  25. Kenta Fukata, Takashi Washio, Hiroshi Motoda
    A Method to Search ARX Model Orders and Its Application to Sales Dynamics Analysis. [Citation Graph (0, 0)][DBLP]
    ICDM Workshops, 2006, pp:590-595 [Conf]
  26. Huan Liu, Hiroshi Motoda, Lei Yu
    Feature Selection with Selective Sampling. [Citation Graph (0, 0)][DBLP]
    ICML, 2002, pp:395-402 [Conf]
  27. Takashi Washio, Hiroshi Motoda, Yuji Niwa
    Enhancing the Plausibility of Law Equation Discovery. [Citation Graph (0, 0)][DBLP]
    ICML, 2000, pp:1127-1134 [Conf]
  28. Masahiro Terabe, Takashi Washio, Hiroshi Motoda
    S3Bagging: Fast Classifier Induction Method with Subsampling and Bagging. [Citation Graph (0, 0)][DBLP]
    IDA, 2001, pp:177-186 [Conf]
  29. Hiroshi Motoda, Kenichi Yoshida
    Machine Learning Techniques to Make Computers Easier to Use. [Citation Graph (0, 0)][DBLP]
    IJCAI, 1997, pp:1622-1631 [Conf]
  30. Takashi Washio, Fuminori Adachi, Hiroshi Motoda
    Discovering Time Differential Law Equations Containing Hidden State Variables and Chaotic Dynamics. [Citation Graph (0, 0)][DBLP]
    IJCAI, 2005, pp:1642-1644 [Conf]
  31. Takashi Washio, Hiroshi Motoda
    Discovering Admissible Models of Complex Systems Based on Scale-Types and Idemtity Constraints. [Citation Graph (0, 0)][DBLP]
    IJCAI (2), 1997, pp:810-819 [Conf]
  32. Takashi Washio, Hiroshi Motoda, Niwa Yuji
    Discovering Admissible Model Equations from Observed Data Based on Scale-Types and Identity Constrains. [Citation Graph (0, 0)][DBLP]
    IJCAI, 1999, pp:772-779 [Conf]
  33. Naoyuki Yamada, Hiroshi Motoda
    A Diagnosis Method of Dynamic System Using the Knowledge on System Description. [Citation Graph (0, 0)][DBLP]
    IJCAI, 1983, pp:225-229 [Conf]
  34. Fuminori Adachi, Takashi Washio, Hiroshi Motoda, Atsushi Fujimoto, Hidemitsu Hanafusa
    Development of Generic Search Method Based on Transformation Invariance. [Citation Graph (0, 0)][DBLP]
    ISMIS, 2003, pp:486-495 [Conf]
  35. Takayuki Ikeda, Takashi Washio, Hiroshi Motoda
    Basket Analysis on Meningitis Data. [Citation Graph (0, 0)][DBLP]
    JSAI Workshops, 2001, pp:516-524 [Conf]
  36. Takashi Washio, Koutarou Nakanishi, Hiroshi Motoda, Takashi Okada
    Mutagenicity Risk Analysis by Using Class Association Rules. [Citation Graph (0, 0)][DBLP]
    JSAI Workshops, 2005, pp:436-445 [Conf]
  37. 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. [Citation Graph (0, 0)][DBLP]
    JSAI, 2006, pp:305-316 [Conf]
  38. Kenichi Yoshida, Fuminori Adachi, Takashi Washio, Hiroshi Motoda, Teruaki Homma, Akihiro Nakashima, Hiromitsu Fujikawa, Katsuyuki Yamazaki
    Density-based spam detector. [Citation Graph (0, 0)][DBLP]
    KDD, 2004, pp:486-493 [Conf]
  39. Katsutoshi Yada, Hiroshi Motoda, Takashi Washio, Asuka Miyawaki
    Consumer Behavior Analysis by Graph Mining Technique. [Citation Graph (0, 0)][DBLP]
    KES, 2004, pp:800-806 [Conf]
  40. Akito Sakurai, Hiroshi Motoda
    Proving Definite Clauses without Explicit Use of Inductions. [Citation Graph (0, 0)][DBLP]
    LP, 1988, pp:11-26 [Conf]
  41. Masaki Suwa, Hiroshi Motoda
    On dealing with dynamic utility of learned knowledge. [Citation Graph (0, 0)][DBLP]
    Machine Intelligence 14, 1993, pp:113-0 [Conf]
  42. Masaki Suwa, Hiroshi Motoda
    Learning Perceptually Chunked Macro Operators. [Citation Graph (0, 0)][DBLP]
    Machine Intelligence 13, 1994, pp:419-440 [Conf]
  43. Kenichi Yoshida, Hiroshi Motoda
    Tables, Graphs and Logic for Induction. [Citation Graph (0, 0)][DBLP]
    Machine Intelligence 15, 1995, pp:298-311 [Conf]
  44. Manoranjan Dash, Huan Liu, Hiroshi Motoda
    Consistency Based Feature Selection. [Citation Graph (0, 0)][DBLP]
    PAKDD, 2000, pp:98-109 [Conf]
  45. Warodom Geamsakul, Takashi Matsuda, Tetsuya Yoshida, Hiroshi Motoda, Takashi Washio
    Classifier Construction by Graph-Based Induction for Graph-Structured Data. [Citation Graph (0, 0)][DBLP]
    PAKDD, 2003, pp:52-62 [Conf]
  46. Akihiro Inokuchi, Takashi Washio, Hiroshi Motoda, Kouhei Kumasawa, Naohide Arai
    Basket Analysis for Graph Structured Data. [Citation Graph (0, 0)][DBLP]
    PAKDD, 1999, pp:420-431 [Conf]
  47. Huan Liu, Lei Yu, Manoranjan Dash, Hiroshi Motoda
    Active Feature Selection Using Classes. [Citation Graph (0, 0)][DBLP]
    PAKDD, 2003, pp:474-485 [Conf]
  48. Amit Mandvikar, Huan Liu, Hiroshi Motoda
    Compact Dual Ensembles for Active Learning. [Citation Graph (0, 0)][DBLP]
    PAKDD, 2004, pp:293-297 [Conf]
  49. Takashi Matsuda, Tadashi Horiuchi, Hiroshi Motoda, Takashi Washio
    Extension of Graph-Based Induction for General Graph Structured Data. [Citation Graph (0, 0)][DBLP]
    PAKDD, 2000, pp:420-431 [Conf]
  50. Hiroshi Motoda
    Computer Assisted Discovery of First Principle Equations from Numeric Data (Abstract). [Citation Graph (0, 0)][DBLP]
    PAKDD, 1999, pp:2- [Conf]
  51. Phu Chien Nguyen, Kouzou Ohara, Akira Mogi, Hiroshi Motoda, Takashi Washio
    Constructing Decision Trees for Graph-Structured Data by Chunkingless Graph-Based Induction. [Citation Graph (0, 0)][DBLP]
    PAKDD, 2006, pp:390-399 [Conf]
  52. Phu Chien Nguyen, Kouzou Ohara, Hiroshi Motoda, Takashi Washio
    Cl-GBI: A Novel Approach for Extracting Typical Patterns from Graph-Structured Data. [Citation Graph (0, 0)][DBLP]
    PAKDD, 2005, pp:639-649 [Conf]
  53. Masahiro Terabe, Osamu Katai, Tetsuo Sawaragi, Takashi Washio, Hiroshi Motoda
    A Data Pre-processing Method Using Association Rules of Attributes for Improving Decision Tree. [Citation Graph (0, 0)][DBLP]
    PAKDD, 1999, pp:143-147 [Conf]
  54. Takuya Wada, Tadashi Horiuchi, Hiroshi Motoda, Takashi Washio
    Characterization of Default Knowledge in Ripple Down Rules Method. [Citation Graph (0, 0)][DBLP]
    PAKDD, 1999, pp:284-295 [Conf]
  55. Takuya Wada, Hiroshi Motoda, Takashi Washio
    Knowledge Acquisition from Both Human Expert and Data. [Citation Graph (0, 0)][DBLP]
    PAKDD, 2001, pp:550-561 [Conf]
  56. Takashi Washio, Hiroshi Motoda
    Mining Association Rules for Estimation and Prediction. [Citation Graph (0, 0)][DBLP]
    PAKDD, 1998, pp:417-419 [Conf]
  57. Akihiro Inokuchi, Takashi Washio, Hiroshi Motoda
    An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data. [Citation Graph (0, 0)][DBLP]
    PKDD, 2000, pp:13-23 [Conf]
  58. Phu Chien Nguyen, Takashi Washio, Kouzou Ohara, Hiroshi Motoda
    Using a Hash-Based Method for Apriori-Based Graph Mining. [Citation Graph (0, 0)][DBLP]
    PKDD, 2004, pp:349-361 [Conf]
  59. Takashi Washio, Koutarou Nakanishi, Hiroshi Motoda
    Deriving Class Association Rules Based on Levelwise Subspace Clustering. [Citation Graph (0, 0)][DBLP]
    PKDD, 2005, pp:692-700 [Conf]
  60. Keisei Fujiwara, Tetsuya Yoshida, Hiroshi Motoda, Takashi Washio
    Case Generation Method for Constructing an RDR Knowledge Base. [Citation Graph (0, 0)][DBLP]
    PRICAI, 2002, pp:228-237 [Conf]
  61. Takashi Matsuda, Hiroshi Motoda, Tetsuya Yoshida, Takashi Washio
    Knowledge Discovery from Structured Data by Beam-Wise Graph-Based Induction. [Citation Graph (0, 0)][DBLP]
    PRICAI, 2002, pp:255-264 [Conf]
  62. 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. [Citation Graph (0, 0)][DBLP]
    PRICAI, 2002, pp:218-227 [Conf]
  63. Takashi Washio, Hiroshi Motoda
    A History-Oriented Envisioning Method. [Citation Graph (0, 0)][DBLP]
    PRICAI, 1996, pp:312-323 [Conf]
  64. Takashi Washio, Atsushi Fujimoto, Hiroshi Motoda
    A Framework of Numerical Basket Analysis. [Citation Graph (0, 0)][DBLP]
    SAINT Workshops, 2005, pp:340-343 [Conf]
  65. Kenichi Yoshida, Fuminori Adachi, Takashi Washio, Hiroshi Motoda, Teruaki Homma, Akihiro Nakashima, Hiromitsu Fujikawa, Katsuyuki Yamazaki
    Memory Management of Density-Based Spam Detector. [Citation Graph (0, 0)][DBLP]
    SAINT, 2005, pp:370-376 [Conf]
  66. Atsuo Kawaguchi, Shingo Nishioka, Hiroshi Motoda
    A Flash-Memory Based File System. [Citation Graph (0, 0)][DBLP]
    USENIX Winter, 1995, pp:155-164 [Conf]
  67. Shingo Nishioka, Atsuo Kawaguchi, Hiroshi Motoda
    Process Labeled Kernel Profiling: A New Facility to Profile System Activities. [Citation Graph (0, 0)][DBLP]
    USENIX Annual Technical Conference, 1996, pp:295-306 [Conf]
  68. Makoto Tsukada, Takashi Washio, Hiroshi Motoda
    Automatic Web-Page Classification by Using Machine Learning Methods. [Citation Graph (0, 0)][DBLP]
    Web Intelligence, 2001, pp:303-313 [Conf]
  69. Kiyoto Takabayashi, Phu Chien Nguyen, Kouzou Ohara, Hiroshi Motoda, Takashi Washio
    Extracting Discriminative Patterns from Graph Structured Data Using Constrained Search. [Citation Graph (0, 0)][DBLP]
    PKAW, 2006, pp:64-74 [Conf]
  70. Byeong Ho Kang, Kenichi Yoshida, Hiroshi Motoda, Paul Compton
    Help Desk System with Intelligent Interface. [Citation Graph (0, 0)][DBLP]
    Applied Artificial Intelligence, 1997, v:11, n:7-8, pp:611-631 [Journal]
  71. Takashi Matsuda, Hiroshi Motoda, Takashi Washio
    Graph-based induction and its applications. [Citation Graph (0, 0)][DBLP]
    Advanced Engineering Informatics, 2002, v:16, n:2, pp:135-143 [Journal]
  72. Huan Liu, Hiroshi Motoda, Lei Yu
    A selective sampling approach to active feature selection. [Citation Graph (0, 0)][DBLP]
    Artif. Intell., 2004, v:159, n:1-2, pp:49-74 [Journal]
  73. Hiroshi Motoda, Kenichi Yoshida
    Machine Learning Techniques to Make Computers Easier to Use. [Citation Graph (0, 0)][DBLP]
    Artif. Intell., 1998, v:103, n:1-2, pp:295-321 [Journal]
  74. Kenichi Yoshida, Hiroshi Motoda
    CLIP: Concept Learning from Inference Patterns. [Citation Graph (0, 0)][DBLP]
    Artif. Intell., 1995, v:75, n:1, pp:63-92 [Journal]
  75. Masaki Sssuwa, Hiroshi Motoda
    PCLEARN: A Computer Model for Learning Perceptual Chunks. [Citation Graph (0, 0)][DBLP]
    AI Commun., 1994, v:7, n:2, pp:114-125 [Journal]
  76. Huan Liu, Hiroshi Motoda
    On Issues of Instance Selection. [Citation Graph (0, 0)][DBLP]
    Data Min. Knowl. Discov., 2002, v:6, n:2, pp:115-130 [Journal]
  77. Atsuo Kawaguchi, Hiroshi Motoda, Riichiro Mizoguchi
    Interview-Based Knowledge Acquisition Using Dynamic Analysis. [Citation Graph (0, 0)][DBLP]
    IEEE Expert, 1991, v:6, n:5, pp:47-60 [Journal]
  78. Huan Liu, Hiroshi Motoda
    Guest Editors' Introduction: Feature Transformation and Subset Selection. [Citation Graph (0, 0)][DBLP]
    IEEE Intelligent Systems, 1998, v:13, n:2, pp:26-28 [Journal]
  79. Riichiro Mizoguchi, Hiroshi Motoda
    Expert Systems Research in Japan. [Citation Graph (0, 0)][DBLP]
    IEEE Expert, 1995, v:10, n:4, pp:14-23 [Journal]
  80. Hiroshi Motoda
    The Current Status of Expert System Development and Related Technologies in Japan. [Citation Graph (0, 0)][DBLP]
    IEEE Expert, 1990, v:5, n:4, pp:3-11 [Journal]
  81. Hiroshi Motoda, Riichiro Mizoguchi, John H. Boose, Brian R. Gaines
    Knowledge Acquisition for Knowledge-Based Systems. [Citation Graph (0, 0)][DBLP]
    IEEE Expert, 1991, v:6, n:4, pp:53-64 [Journal]
  82. Warodom Geamsakul, Tetsuya Yoshida, Kouzou Ohara, Hiroshi Motoda, Hideto Yokoi, Katsuhiko Takabayashi
    Constructing a Decision Tree for Graph-Structured Data and its Applications. [Citation Graph (0, 0)][DBLP]
    Fundam. Inform., 2005, v:66, n:1-2, pp:131-160 [Journal]
  83. Akihiro Inokuchi, Takashi Washio, Hiroshi Motoda
    A General Framework for Mining Frequent Subgraphs from Labeled Graphs. [Citation Graph (0, 0)][DBLP]
    Fundam. Inform., 2005, v:66, n:1-2, pp:53-82 [Journal]
  84. Tetsuya Yoshida, Takuya Wada, Hiroshi Motoda, Takashi Washio
    Adaptive Ripple Down Rules method based on minimum description length principle. [Citation Graph (0, 0)][DBLP]
    Intell. Data Anal., 2004, v:8, n:3, pp:239-265 [Journal]
  85. Takashi Washio, Hiroshi Motoda, Yuji Niwa
    Enhancing the plausibility of law equation discovery through cross check among multiple scale-type-based models. [Citation Graph (0, 0)][DBLP]
    J. Exp. Theor. Artif. Intell., 2005, v:17, n:1-2, pp:129-143 [Journal]
  86. Masahiro Terabe, Takashi Washio, Hiroshi Motoda, Osamu Katai, Tetsuo Sawaragi
    Attribute Generation Based on Association Rules. [Citation Graph (0, 0)][DBLP]
    Knowl. Inf. Syst., 2002, v:4, n:3, pp:329-349 [Journal]
  87. Takuya Wada, Tadashi Horiuchi, Hiroshi Motoda, Takashi Washio
    A Description Length-Based Decision Criterion for Default Knowledge in the Ripple Down Rules Method. [Citation Graph (0, 0)][DBLP]
    Knowl. Inf. Syst., 2001, v:3, n:2, pp:146-167 [Journal]
  88. Hing-Yan Lee, Hongjun Lu, Hiroshi Motoda
    Knowledge discovery and data mining. [Citation Graph (0, 0)][DBLP]
    Knowl.-Based Syst., 1998, v:10, n:7, pp:401-402 [Journal]
  89. Takashi Washio, Hiroshi Motoda
    Discovery of first-principle equations based on scale-type-based and data-driven reasoning. [Citation Graph (0, 0)][DBLP]
    Knowl.-Based Syst., 1998, v:10, n:7, pp:403-411 [Journal]
  90. Akihiro Inokuchi, Takashi Washio, Hiroshi Motoda
    Complete Mining of Frequent Patterns from Graphs: Mining Graph Data. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2003, v:50, n:3, pp:321-354 [Journal]
  91. Nada Lavrac, Hiroshi Motoda, Tom Fawcett
    Editorial: Data Mining Lessons Learned. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2004, v:57, n:1-2, pp:5-11 [Journal]
  92. Nada Lavrac, Hiroshi Motoda, Tom Fawcett, Robert Holte, Pat Langley, Pieter W. Adriaans
    Introduction: Lessons Learned from Data Mining Applications and Collaborative Problem Solving. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2004, v:57, n:1-2, pp:13-34 [Journal]
  93. Hiroshi Motoda, Setsuo Arikawa
    Special Feature on Discovery Science. [Citation Graph (0, 0)][DBLP]
    New Generation Comput., 2000, v:18, n:1, pp:13-16 [Journal]
  94. Takashi Washio, Hiroshi Motoda
    State of the art of graph-based data mining. [Citation Graph (0, 0)][DBLP]
    SIGKDD Explorations, 2003, v:5, n:1, pp:59-68 [Journal]
  95. Setsuo Arikawa, Koichi Furukawa, Shinichi Morishita, Hiroshi Motoda
    Preface. [Citation Graph (0, 0)][DBLP]
    Theor. Comput. Sci., 2003, v:292, n:2, pp:343-344 [Journal]
  96. Toshiko Wakaki, Hiroyuki Itakura, Masaki Tamura, Hiroshi Motoda, Takashi Washio
    A study on rough set-aided feature selection for automatic web-page classification. [Citation Graph (0, 0)][DBLP]
    Web Intelligence and Agent Systems, 2006, v:4, n:4, pp:431-441 [Journal]
  97. Takashi Washio, Hiroshi Motoda
    Communicability Criteria of Law Equations Discovery. [Citation Graph (0, 0)][DBLP]
    Computational Discovery of Scientific Knowledge, 2007, pp:98-119 [Conf]
  98. Hiroshi Motoda
    Pattern Discovery from Graph-Structured Data - A Data Mining Perspective. [Citation Graph (0, 0)][DBLP]
    IEA/AIE, 2007, pp:12-22 [Conf]
  99. Yang Sok Kim, Byeong Ho Kang, Paul Compton, Hiroshi Motoda
    Search engine retrieval of changing information. [Citation Graph (0, 0)][DBLP]
    WWW, 2007, pp:1195-1196 [Conf]

  100. Minimizing the Spread of Contamination by Blocking Links in a Network. [Citation Graph (, )][DBLP]


  101. Learning to Predict Opinion Share in Social Networks. [Citation Graph (, )][DBLP]


  102. Discovering Influential Nodes for SIS Models in Social Networks. [Citation Graph (, )][DBLP]


  103. Efficient Estimation of Influence Functions for SIS Model on Social Networks. [Citation Graph (, )][DBLP]


  104. Community analysis of influential nodes for information diffusion on a social network. [Citation Graph (, )][DBLP]


  105. Effective Visualization of Information Diffusion Process over Complex Networks. [Citation Graph (, )][DBLP]


  106. Selecting Information Diffusion Models over Social Networks for Behavioral Analysis. [Citation Graph (, )][DBLP]


  107. Solving the Contamination Minimization Problem on Networks for the Linear Threshold Model. [Citation Graph (, )][DBLP]


  108. Efficient Estimation of Cumulative Influence for Multiple Activation Information Diffusion Model with Continuous Time Delay. [Citation Graph (, )][DBLP]


  109. Pruning Strategies Based on the Upper Bound of Information Gain for Discriminative Subgraph Mining. [Citation Graph (, )][DBLP]


  110. What Does an Information Diffusion Model Tell about Social Network Structure?. [Citation Graph (, )][DBLP]


  111. Finding Relation between PageRank and Voter Model. [Citation Graph (, )][DBLP]


  112. Acquiring Expected Influence Curve from Single Diffusion Sequence. [Citation Graph (, )][DBLP]


  113. Learning Continuous-Time Information Diffusion Model for Social Behavioral Data Analysis. [Citation Graph (, )][DBLP]


  114. Behavioral Analyses of Information Diffusion Models by Observed Data of Social Network. [Citation Graph (, )][DBLP]


  115. Graph-based induction as a unified learning framework. [Citation Graph (, )][DBLP]


  116. Extracting influential nodes on a social network for information diffusion. [Citation Graph (, )][DBLP]


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