BibSLEIGH — Xindong

Travelled to:

1 × Canada
1 × Germany
1 × Israel
1 × Italy
1 × South Korea
1 × Switzerland
1 × United Kingdom
2 × Australia
3 × China
9 × USA

Collaborated with:

X.Zhu ∅ W.Ding S.Zhang Z.Li K.Yu C.Zhang P.Måhlén H.Li H.Liu X.Hu D.Wang Y.Yang G.Lee J.Chon Q.Chen R.Relue H.Huang D.Urpani J.Sykes D.Wang D.L.Small S.Islam D.A.Simovici H.Wang Z.Lu J.Bongard T.Cao S.Wang X.Hu F.Chen P.Li H.Wang K.Q.Zhu Z.Wang Y.Song L.Cao G.Wei W.Ye J.Pei P.Chen

Talks about:

cluster (6) select (4) featur (4) rule (4) data (4) stream (3) mine (3) larg (3) knowledg (2) hierarch (2)

Person: Xindong Wu

DBLP: Wu:Xindong

Facilitated 2 volumes:

Contributed to:

2015

2014

2013

2012

2011

2010

2007

2006

2005

2003

2002

2001

1996

1995

Wrote 23 papers:

KDD-2015-YuW0PSIW #bound #markov #multi: Tornado Forecasting with Multiple Markov Boundaries (KY, DW, WD, JP, DLS, SI, XW), pp. 2237–2246.
SAC-2014-LiW #clustering #matrix #multi: Single multiplicatively updated matrix factorization for co-clustering (ZL, XW), pp. 97–104.
SAC-2014-LiWL #learning #mobile #online #recognition: Online learning with mobile sensor data for user recognition (HGL, XW, ZL), pp. 64–70.
CIKM-2013-LiWZWW #probability #scalability #similarity: Computing term similarity by large probabilistic isA knowledge (PPL, HW, KQZ, ZW, XW), pp. 1401–1410.
KDD-2013-WangDYWCSI #clustering #data mining #framework #identification #mining #towards: Towards long-lead forecasting of extreme flood events: a data mining framework for precipitation cluster precursors identification (DW, WD, KY, XW, PC, DLS, SI), pp. 1285–1293.
KDD-2012-SongCWWYD #analysis #behaviour: Coupled behavior analysis for capturing coupling relationships in group-based market manipulations (YS, LC, XW, GW, WY, WD), pp. 976–984.
KDD-2012-YuDSW #feature model #mining #streaming: Mining emerging patterns by streaming feature selection (KY, WD, DAS, XW), pp. 60–68.
CIKM-2011-LiuWZ #clustering #feature model #using: Feature selection using hierarchical feature clustering (HL, XW, SZ), pp. 979–984.
ICML-2010-WuYWD #feature model #online #streaming: Online Streaming Feature Selection (XW, KY, HW, WD), pp. 1159–1166.
KDD-2010-LuWZB: Ensemble pruning via individual contribution ordering (ZL, XW, XZ, JB), pp. 871–880.
SAC-2010-CaoWWH #approach #composition #named #network #social: OASNET: an optimal allocation approach to influence maximization in modular social networks (TC, XW, SW, XH), pp. 1088–1094.
MLDM-2007-HuWW #clustering: Varying Density Spatial Clustering Based on a Hierarchical Tree (XH, DW, XW), pp. 188–202.
EDOC-2006-Wu #web: User-Centered Agents for Structured Information Location on the Web (XW).
ICPR-v3-2006-ZhuW #ranking #scalability: Scalable Representative Instance Selection and Ranking (XZ, XW), pp. 352–355.
KDD-2006-ZhangCWZ #clustering #concept #identification: Identifying bridging rules between conceptual clusters (SZ, FC, XW, CZ), pp. 815–820.
KDD-2005-YangWZ #data type #predict: Combining proactive and reactive predictions for data streams (YY, XW, XZ), pp. 710–715.
SAC-2005-LeeWC #clustering #performance: Rearranging data objects for efficient and stable clustering (GL, XW, JC), pp. 519–523.
ICML-2003-ZhuWC #dataset #scalability: Eliminating Class Noise in Large Datasets (XZ, XW, QC), pp. 920–927.
ICML-2002-WuZZ #mining: Mining Both Positive and Negative Association Rules (XW, CZ, SZ), pp. 658–665.
SEKE-2002-Wu #database #information management #scalability: Knowledge discovery in very large databases (XW), p. 15.
CIKM-2001-RelueWH #generative #performance #runtime: Efficient Runtime Generation of Association Rules (RR, XW, HH), pp. 466–473.
KDD-1996-UrpaniWS #induction #named: RITIO — Rule Induction Two In One (DU, XW, JS), pp. 339–342.
KDD-1995-WuM #fuzzy #induction: Fuzzy Interpretation of Induction Results (XW, PM), pp. 325–330.