BibSLEIGH — Bing

Travelled to:

1 × France
1 × Germany
1 × India
18 × USA
2 × Australia
2 × Canada
2 × The Netherlands
3 × China

Collaborated with:

W.Hsu Y.Ma P.S.Yu Z.Chen M.Hu X.Li N.Jindal K.Zhao L.Zhang X.Ding A.Mukherjee W.Xiao W.S.Lee K.Wang S.Chen E.Lim Y.Zhang J.Benkler X.Li T.M.Tirpak T.Phang A.Tuzhilin T.Qian R.Jones K.L.Klinkner R.L.Grossman Y.Zhai L.Yi Y.Xia C.Xu S.H.W.Tay M.Hsu M.Castellanos R.Ghosh M.Zhong G.He A.Schaller H.Wu M.Lee T.W.Ling Y.He Z.Jiang H.Zhao J.Wang C.T.Yu W.Meng V.Nguyen H.W.Lauw C.K.Wong S.M.Lee Q.Li H.Xiong J.Srivastava P.C.Sheu A.Kumar J.Wang Etaba Assigana E.Chang Seungsuk Cho Vivek Kotecha Hannah Turner Y.Zhang M.G.Christel S.M.Stevens

Talks about:

mine (15) use (13) rule (11) discov (7) data (7) text (6) document (5) associ (5) topic (5) opinion (4)

Person: Bing Liu

DBLP: Liu:Bing

Facilitated 2 volumes:

Contributed to:

2014

2013

2012

2011

2010

2009

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

2014

Wrote 45 papers:

ICML-c2-2014-Chen0 #big data #learning #modelling #topic #using: Topic Modeling using Topics from Many Domains, Lifelong Learning and Big Data (ZC, BL), pp. 703–711.
KDD-2014-Chen0 #big data #documentation #mining #topic: Mining topics in documents: standing on the shoulders of big data (ZC, BL), pp. 1116–1125.
SIGIR-2014-QianL0H: Co-training on authorship attribution with very fewlabeled examples: methods vs. views (TQ, BL, MZ, GH), pp. 903–906.
CIKM-2013-ChenMLHCG #topic #using: Discovering coherent topics using general knowledge (ZC, AM, BL, MH, MC, RG), pp. 209–218.
KDD-2013-MukherjeeKLWHCG #behaviour #using: Spotting opinion spammers using behavioral footprints (AM, AK, BL, JW, MH, MC, RG), pp. 632–640.
CIKM-2012-WangYYLM: Diversionary comments under political blog posts (JW, CTY, PSY, BL, WM), pp. 1789–1793.
KDD-2012-MukherjeeL #mining: Mining contentions from discussions and debates (AM, BL), pp. 841–849.
HT-2011-ZhangL #documentation #set: Entity set expansion in opinion documents (LZ, BL), pp. 281–290.
CIKM-2010-JindalLL #overview #using: Finding unusual review patterns using unexpected rules (NJ, BL, EPL), pp. 1549–1552.
CIKM-2010-LimNJLL #behaviour #detection #overview #rating #using: Detecting product review spammers using rating behaviors (EPL, VAN, NJ, BL, HWL), pp. 939–948.
CIKM-2009-QianLLXSS #community #development #topic #what: What’s behind topic formation and development: a perspective of community core groups (TQ, QL, BL, HX, JS, PCYS), pp. 1843–1846.
KDD-2009-DingLZ #mining: Entity discovery and assignment for opinion mining applications (XD, BL, LZ), pp. 1125–1134.
SIGIR-2007-DingL #mining: The utility of linguistic rules in opinion mining (XD, BL), pp. 811–812.
SIGIR-2007-ZhangL #classification #semantics: Semantic text classification of disease reporting (YZ, BL), pp. 747–748.
CIKM-2006-LiuJK #clustering #query: Measuring the meaning in time series clustering of text search queries (BL, RJ, KLK), pp. 836–837.
KDD-2006-LiuZBX #analysis #using: Rule interestingness analysis using OLAP operations (BL, KZ, JB, WX), pp. 297–306.
KDD-2006-ZhaoLBX #data mining #identification #mining: Opportunity map: identifying causes of failure — a deployed data mining system (KZ, BL, JB, WX), pp. 892–901.
SIGIR-2006-JindalL #comparative #documentation #identification: Identifying comparative sentences in text documents (NJ, BL), pp. 244–251.
CIKM-2005-LiL #community #mining: Mining community structure of named entities from free text (XL, BL), pp. 275–276.
CIKM-2005-ZhaoLTX #framework #identification #performance #visualisation: Opportunity map: a visualization framework for fast identification of actionable knowledge (KZ, BL, TMT, WX), pp. 60–67.
KDD-2004-HuL #mining: Mining and summarizing customer reviews (MH, BL), pp. 168–177.
KDD-2004-ZhaoLTS #coordination #design #named #parallel #testing #using: V-Miner: using enhanced parallel coordinates to mine product design and test data (KZ, BL, TMT, AS), pp. 494–502.
ICML-2003-LeeL #learning #using: Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression (WSL, BL), pp. 448–455.
KDD-2003-LiuGZ #mining #web: Mining data records in Web pages (BL, RLG, YZ), pp. 601–606.
KDD-2003-YiLL #data mining #mining #web: Eliminating noisy information in Web pages for data mining (LY, BL, XL), pp. 296–305.
CIKM-2002-LiPHL #internet #using: Using micro information units for internet search (XL, THP, MH, BL), pp. 566–573.
ICML-2002-LiuLYL #classification #documentation: Partially Supervised Classification of Text Documents (BL, WSL, PSY, XL), pp. 387–394.
KDD-2002-TuzhilinL #multi #query #set: Querying multiple sets of discovered rules (AT, BL), pp. 52–60.
KDD-2002-WuPLL #approach #categorisation #refinement: A refinement approach to handling model misfit in text categorization (HW, THP, BL, XL), pp. 207–216.
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-2001-LiuMY #web: Discovering unexpected information from your competitors’ web sites (BL, YM, PSY), pp. 144–153.
CIKM-2000-LiuXY #clustering: Clustering Through Decision Tree Construction (BL, YX, PSY), pp. 20–29.
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.
KDD-2000-MaLWYL #data mining #mining #student #using: Targeting the right students using data mining (YM, BL, CKW, PSY, SML), pp. 457–464.
CIKM-1999-WangXL #clustering #scalability #transaction #using: Clustering Transactions Using Large Items (KW, CX, BL), pp. 483–490.
ICDAR-1999-HeJLZ #documentation #image #retrieval: Content-based Index and Retrieval Method of Chinese Document Images (YH, ZJ, BL, HZ), pp. 685–688.
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-1998-WangTL: Interestingness-Based Interval Merger for Numeric Association Rules (KW, SHWT, BL), pp. 121–128.
KDD-1997-LiuHC #classification #using: Using General Impressions to Analyze Discovered Classification Rules (BL, WH, SC), pp. 31–36.
CHI-PLAY-2014-AssiganaCCKLTZC #behaviour #game studies: TF-CBT triangle of life: a game to help with cognitive behavioral therapy (EA, EC, SC, VK, BL, HT, YZ, MGC, SMS), pp. 9–16.