On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms K Yamanishi, JI Takeuchi, G Williams, P Milne Proceedings of the sixth ACM SIGKDD international conference on Knowledge …, 2000 | 951 | 2000 |

A unifying framework for detecting outliers and change points from non-stationary time series data K Yamanishi, J Takeuchi Proceedings of the eighth ACM SIGKDD international conference on Knowledge …, 2002 | 802* | 2002 |

Mining product reputations on the web S Morinaga, K Yamanishi, K Tateishi, T Fukushima Proceedings of the eighth ACM SIGKDD international conference on Knowledge …, 2002 | 677 | 2002 |

Dynamic syslog mining for network failure monitoring K Yamanishi, Y Maruyama Proceedings of the eleventh ACM SIGKDD international conference on Knowledge …, 2005 | 288 | 2005 |

Discovering outlier filtering rules from unlabeled data: combining a supervised learner with an unsupervised learner K Yamanishi, J Takeuchi Proceedings of the seventh ACM SIGKDD international conference on Knowledge …, 2001 | 186 | 2001 |

Tracking dynamics of topic trends using a finite mixture model S Morinaga, K Yamanishi Proceedings of the tenth ACM SIGKDD international conference on Knowledge …, 2004 | 169 | 2004 |

Mining open answers in questionnaire data K Yamanishi, H Li IEEE Intelligent Systems 17 (5), 58-63, 2002 | 169* | 2002 |

A learning criterion for stochastic rules K Yamanishi Machine Learning 9, 165-203, 1992 | 165 | 1992 |

Discovering emerging topics in social streams via link-anomaly detection T Takahashi, R Tomioka, K Yamanishi IEEE Transactions on Knowledge and Data Engineering 26 (1), 120-130, 2012 | 140 | 2012 |

Topic analysis using a finite mixture model H Li, K Yamanishi Information processing & management 39 (4), 521-541, 2003 | 139 | 2003 |

A decision-theoretic extension of stochastic complexity and its applications to learning K Yamanishi IEEE Transactions on Information Theory 44 (4), 1424-1439, 1998 | 133 | 1998 |

Detection of longitudinal visual field progression in glaucoma using machine learning S Yousefi, T Kiwaki, Y Zheng, H Sugiura, R Asaoka, H Murata, H Lemij, ... American journal of ophthalmology 193, 71-79, 2018 | 117 | 2018 |

Document classification method and apparatus therefor H Li, K Yamanishi US Patent 6,094,653, 2000 | 116 | 2000 |

Text classification using ESC-based stochastic decision lists H Li, K Yamanishi Proceedings of the eighth international conference on Information and …, 1999 | 104 | 1999 |

Detection of abnormal behavior using probabilistic distribution estimation Y Matsunaga, K Yamanishi US Patent 7,561,991, 2009 | 74 | 2009 |

Distributed cooperative Bayesian learning strategies K Yamanishi Proceedings of the tenth annual conference on Computational learning theory …, 1997 | 72 | 1997 |

Network anomaly detection based on eigen equation compression S Hirose, K Yamanishi, T Nakata, R Fujimaki Proceedings of the 15th ACM SIGKDD international conference on Knowledge …, 2009 | 70 | 2009 |

Document classification using a finite mixture model H Li, K Yamanishi arXiv preprint cmp-lg/9705005, 1997 | 66 | 1997 |

Efficient computation of normalized maximum likelihood codes for Gaussian mixture models with its applications to clustering S Hirai, K Yamanishi IEEE Transactions on Information Theory 59 (11), 7718-7727, 2013 | 63* | 2013 |

データマイニングによる異常検知 山西健司 共立出版 37 (3), 13-44, 2009 | 57 | 2009 |