`Travelled to:`

1 × Australia

1 × Canada

1 × China

1 × France

7 × USA

`Collaborated with:`

J.Takeuchi S.Morinaga H.Li S.Hirai Y.Maruyama A.Konagaya S.Hirose T.Nakata R.Fujimaki K.Tateishi T.Fukushima G.J.Williams P.Milne S.Maya K.Morino H.Murata R.Asaoka

`Talks about:`

use (5) detect (4) mine (4) outlier (3) data (3) base (3) unsupervis (2) stochast (2) network (2) learner (2)

## Person: Kenji Yamanishi

### DBLP: Yamanishi:Kenji

### Contributed to:

### Wrote 13 papers:

- KDD-2015-MayaMMAY #clustering #using
- Discovery of Glaucoma Progressive Patterns Using Hierarchical MDL-Based Clustering (SM, KM, HM, RA, KY), pp. 1979–1988.
- KDD-2012-HiraiY #clustering #detection #normalisation #using
- Detecting changes of clustering structures using normalized maximum likelihood coding (SH, KY), pp. 343–351.
- KDD-2009-HiroseYNF #detection #equation #network
- Network anomaly detection based on Eigen equation compression (SH, KY, TN, RF), pp. 1185–1194.
- KDD-2005-YamanishiM #mining #monitoring #network
- Dynamic syslog mining for network failure monitoring (KY, YM), pp. 499–508.
- KDD-2004-MorinagaY #finite #roadmap #topic #using
- Tracking dynamics of topic trends using a finite mixture model (SM, KY), pp. 811–816.
- KDD-2003-MorinagaYT #distributed #mining
- Distributed cooperative mining for information consortia (SM, KY, JiT), pp. 619–624.
- KDD-2002-MorinagaYTF #mining #web
- Mining product reputations on the Web (SM, KY, KT, TF), pp. 341–349.
- KDD-2002-YamanishiT #detection #framework
- A unifying framework for detecting outliers and change points from non-stationary time series data (KY, JiT), pp. 676–681.
- KDD-2001-LiY #mining
- Mining from open answers in questionnaire data (HL, KY), pp. 443–449.
- KDD-2001-YamanishiT
- Discovering outlier filtering rules from unlabeled data: combining a supervised learner with an unsupervised learner (KY, JiT), pp. 389–394.
- KDD-2000-YamanishiTWM #algorithm #detection #finite #learning #online #using
- On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms (KY, JiT, GJW, PM), pp. 320–324.
- CIKM-1999-LiY #classification #probability #using
- Text Classification Using ESC-based Stochastic Decision Lists (HL, KY), pp. 122–130.
- ML-1991-YamanishiK #learning #probability #search-based #sequence
- Learning Stochastic Motifs from Genetic Sequences (KY, AK), pp. 467–471.