| On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms K Yamanishi, JI Takeuchi, G Williams, P Milne Data Mining and Knowledge Discovery 8 (3), 275-300, 2004 | 693 | 2004 |
| 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 | 561 | 2002 |
| 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 | 296 | 2002 |
| A unifying framework for detecting outliers and change points from time series J Takeuchi, K Yamanishi IEEE transactions on Knowledge and Data Engineering 18 (4), 482-492, 2006 | 269 | 2006 |
| Dynamic syslog mining for network failure monitoring K Yamanishi, Y Maruyama Proceedings of the eleventh ACM SIGKDD international conference on Knowledge …, 2005 | 210 | 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 | 156 | 2001 |
| Mining open answers in questionnaire data K Yamanishi, H Li IEEE Intelligent Systems 17 (5), 58-63, 2002 | 151* | 2002 |
| A learning criterion for stochastic rules K Yamanishi Machine Learning 9 (2-3), 165-203, 1992 | 150 | 1992 |
| 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 | 143 | 2004 |
| A decision-theoretic extension of stochastic complexity and its applications to learning K Yamanishi IEEE Transactions on Information Theory 44 (4), 1424-1439, 1998 | 122 | 1998 |
| 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 | 121 | 2012 |
| Topic analysis using a finite mixture model H Li, K Yamanishi Information processing & management 39 (4), 521-541, 2003 | 116 | 2003 |
| Text classification using ESC-based stochastic decision lists H Li, K Yamanishi Information processing & management 38 (3), 343-361, 2002 | 97 | 2002 |
| Document classification method and apparatus therefor H Li, K Yamanishi US Patent 6,094,653, 2000 | 79 | 2000 |
| Distributed cooperative Bayesian learning strategies K Yamanishi Information and Computation 150 (1), 22-56, 1999 | 69 | 1999 |
| Document classification using a finite mixture model H Li, K Yamanishi arXiv preprint cmp-lg/9705005, 1997 | 58 | 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 | 56 | 2009 |
| 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 | 43* | 2013 |
| Detection of abnormal behavior using probabilistic distribution estimation Y Matsunaga, K Yamanishi US Patent 7,561,991, 2009 | 37 | 2009 |
| Dynamic model selection with its applications to novelty detection K Yamanishi, Y Maruyama IEEE Transactions on Information Theory 53 (6), 2180-2189, 2007 | 32 | 2007 |
