BibSLEIGH — Wynne

Travelled to:

1 × Australia
1 × Ireland
1 × Japan
14 × USA
2 × Canada
2 × China
2 × Germany

Collaborated with:

M.Lee B.Liu Y.Ma W.Chen L.H.Yang F.Li S.Chen C.Xia G.Zhao K.L.Teo G.L.Y.San D.Patel M.Zhang J.Wang J.Dai B.Fang M.Hu K.G.Goh W.Tan J.Chen S.Ng X.Guo B.C.Chua K.Tan X.Yang T.W.Ling H.Hu C.S.Jensen B.Cui B.C.Ooi P.K.Mohanty

Talks about:

mine (11) rule (8) recommend (5) pattern (4) databas (4) effici (4) discov (4) associ (4) base (4) xml (4)

Person: Wynne Hsu

DBLP: Hsu:Wynne

Contributed to:

2015

2014

2013

2012

2011

2008

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

Wrote 32 papers:

SIGMOD-2015-LiLHT #profiling: Linking Temporal Records for Profiling Entities (FL, MLL, WH, WCT), pp. 593–605.
KDD-2014-LiLH #profiling: Entity profiling with varying source reliabilities (FL, MLL, WH), pp. 1146–1155.
CIKM-2013-ZhaoLHCH #network #recommendation #social: Community-based user recommendation in uni-directional social networks (GZ, MLL, WH, WC, HH), pp. 189–198.
KDD-2013-ChenHL #multi #recommendation: Making recommendations from multiple domains (WC, WH, MLL), pp. 892–900.
SIGIR-2013-ChenHL #modelling #recommendation: Modeling user’s receptiveness over time for recommendation (WC, WH, MLL), pp. 373–382.
SIGIR-2013-ChenHL13a #feedback #recommendation: Tagcloud-based explanation with feedback for recommender systems (WC, WH, MLL), pp. 945–948.
ICPR-2012-SanLH #detection: Constrained-MSER detection of retinal pathology (GLYS, MLL, WH), pp. 2059–2062.
SIGIR-2012-ZhaoLHC: Increasing temporal diversity with purchase intervals (GZ, MLL, WH, WC), pp. 165–174.
SIGIR-2011-WeiHL #analysis #framework #recommendation #semantics: A unified framework for recommendations based on quaternary semantic analysis (WC, WH, MLL), pp. 1023–1032.
SIGMOD-2008-PatelHL #classification #mining: Mining relationships among interval-based events for classification (DP, WH, MLL), pp. 393–404.
KDD-2006-ChenHLN #interactive #named #network: NeMoFinder: dissecting genome-wide protein-protein interactions with meso-scale network motifs (JC, WH, MLL, SKN), pp. 106–115.
KDD-2006-ZhangHL #mining: Mining progressive confident rules (MZ, WH, MLL), pp. 803–808.
CIKM-2005-WangHL #database #framework #mining: A framework for mining topological patterns in spatio-temporal databases (JW, WH, MLL), pp. 429–436.
CIKM-2005-XiaHL #estimation #named #nearest neighbour #performance #retrieval: ERkNN: efficient reverse k-nearest neighbors retrieval with local kNN-distance estimation (CX, WH, MLL), pp. 533–540.
KDD-2004-YangLHG #mining #named #performance #query #xml: 2PXMiner: an efficient two pass mining of frequent XML query patterns (LHY, MLL, WH, XG), pp. 731–736.
KDD-2003-HsuDL #database #image #mining: Mining viewpoint patterns in image databases (WH, JD, MLL), pp. 553–558.
VLDB-2003-LeeHJT #approach #bottom-up: Supporting Frequent Updates in R-Trees: A Bottom-Up Approach (MLL, WH, CSJ, BC, KLT), pp. 608–619.
VLDB-2003-YangLH #mining #performance #query #xml: Efficient Mining of XML Query Patterns for Caching (LHY, MLL, WH), pp. 69–80.
CIKM-2002-LeeCHT #evaluation #multi #performance #query #streaming #xml: Efficient evaluation of multiple queries on streaming XML data (MLL, BCC, WH, KLT), pp. 118–125.
CIKM-2002-LeeYHY #clustering #effectiveness #integration #named #xml: XClust: clustering XML schemas for effective integration (MLL, LHY, WH, XY), pp. 292–299.
KDD-2002-FangHL #identification #using: Tumor cell identification using features rules (BF, WH, MLL), pp. 495–500.
VLDB-2002-HsuLOMTX #database: Advanced Database Technologies in a Diabetic Healthcare System (WH, MLL, BCO, PKM, KLT, CX), pp. 1059–1062.
KDD-2001-LiuHM #identification: Identifying non-actionable association rules (BL, WH, YM), pp. 329–334.
KDD-2001-LiuHM01a #set: Discovering the set of fundamental rule changes (BL, WH, YM), pp. 335–340.
KDD-2000-HsuLLL #database #mining: Exploration mining in diabetic patients databases: findings and conclusions (WH, MLL, BL, TWL), pp. 430–436.
KDD-2000-LiuHH #multi #summary: Multi-level organization and summarization of the discovered rules (BL, MH, WH), pp. 208–217.
SIGMOD-2000-HsuLG #image #information management #mining: Image Mining in IRIS: Integrated Retinal Information System (WH, MLL, KGG), p. 593.
KDD-1999-LiuHM: Pruning and Summarizing the Discovered Associations (BL, WH, YM), pp. 125–134.
KDD-1999-LiuHM99a #mining #multi: Mining Association Rules with Multiple Minimum Supports (BL, WH, YM), pp. 337–341.
KDD-1999-LiuHMC #mining #using: Mining Interesting Knowledge Using DM-II (BL, WH, YM, SC), pp. 430–434.
KDD-1998-LiuHM #classification #mining: Integrating Classification and Association Rule Mining (BL, WH, YM), pp. 80–86.
KDD-1997-LiuHC #classification #using: Using General Impressions to Analyze Discovered Classification Rules (BL, WH, SC), pp. 31–36.