Conference of the year: KDD 2009

This is one great co9nference you should attend if you have the time and inclination to check out latest advances in the world of Knowledge discovery. While KXEN ( from whom I consult on social madia) is a Gold Sponser- the following posts on workshops, demos and papers will show you just how much technical stuff as opposed to marketing bullshit and jazz ( as in other confs) is available in this conference. So pack your bags, and Viva La France for a grueling refreshing course in Knowledge Discovery and Text Mining. Incidentally KXEN intend to show their path breaking cutting edge social network analysis software KSN here.

Disclaimer- I am a social media consultant to KXEN.

KDD2009: Workshops

Abstracts

W1 – Statistical and Relational Learning and Mining in Bioinformatics (StReBio’09)

Jan Ramon, Fabrizio Costa, Christophe Costa Florencio, Joost Kok

Bioinformatics is an application domain where information is naturally represented in terms of relations between heterogenous objects. Modern experimentation and data acquisition techniques allow the study of complex interactions in biological systems. This raises interesting challenges because the amount of data is huge,some information can not be observed, and measurements may be noisy.

The StReBio’09 workshop invites contributions concerning applications of statistical relational learning and mining methods in bio-informatics domains. In particular, the workshop invites both regular papers, problem statements and problem solution papers.

W2 – The 3rd International Workshop on Knowledge Discovery from Sensor Data (SensorKDD-2009)

Olufemi Omitaomu, Auroop Ganguly, Joao Gama, Ranga Raju Vatsavai, Mohamed Medhat Gaber and Nitesh V. Chawla

Wide-area sensor infrastructures, remote sensors, RFIDs, and wireless sensor networks yield massive volumes of disparate, dynamic, and geographically distributed data. The Sensor-KDD 2009 workshop solicits papers that describe innovative solutions in offline data mining and/or real-time analysis of sensor or streaming data. Position papers that describe the challenges and requirements for sensor data based knowledge discovery in high-priority application domains, as well as relevant case studies, are particularly encouraged.

W3 – ACM SIGKDD Workshop on CyberSecurity and Intelligence Informatics (CSI-KDD)

Hsinchun Chen, Marc Dacier, Marie-Francine Moens, Gerhard Paaß, Christopher C. Yang

Computer supported communication and infrastructure are integral parts of modern economy. Their security is of incredible importance to a wide variety of practical domains ranging from Internet service providers to the banking industry and e-commerce, from corporate networks to the intelligence community. Of interest to this workshop are novel knowledge discovery methods addressing this field, e.g. adaptive, active or anticipatory approaches integrating new types of contents and protocols. Equally important are innovative applications demonstrating the effectiveness of data mining in solving real-world security problems.

W4 – Workshop on Visual Analytics and Knowledge Discovery (VAKD ’09)

Kai Puolamäki, Heikki Mannila, Alessio Bertone, Silvia Miksch, Mark A. Whiting, Jean Scholtz

The goal of Visual Analytics is to derive insight from massive, dynamic, ambiguous, and often conflicting data; detect the expected and discover the unexpected; provide timely, defensible, and understandable assessments; and communicate the assessment effectively for action. The goal of this workshop is to raise the awareness of the KDD community for the importance of Visual Analytics and bring together researcher from the underlying fields to bridge the gap between them—to write a KDD research roadmap on Visual Analytics.

W5 – The Third International Workshop on Data Mining and Audience Intelligence for Advertising (ADKDD)

Ying Li, Arun C. Surendran, and Dou Shen

Advertising, especially online advertising, is growing rapidly and brings about large volumes of data along with challenging data mining problems. Following on the success of ADKDD 2007 and 2008, ADKDD 2009 is to be held in Paris France, in conjunction with KDD 2009, to provide a high-level international forum for the academic community and the industry to present the state of the art of algorithms and applications of advertising.

We encourage papers that bring up and formalize new research problems in online advertising, or propose novel data mining techniques for existing problems. We plan to cover (but not restricted to) the following areas: Mining for Ad Relevance and Ranking; Audience Intelligence & User Modeling; Content Understanding; Search Engine Marketing, Optimization (SEMs, SEOs) and Other Topics in Advertising. Accepted papers will be achieved in ACM Digital Library and one or two papers will be recommended to SIGKDD Explorations.

W6 – The 3rd Workshop on Social Network Mining and Analysis (SNA-KDD)

Lee Giles, Prasenjit Mitra, Igor Perisic, John Yen, Haizheng Zhang

(Abstract Coming Soon)

W7 – Human Computation Workshop (HCOMP 2009)

Paul Bennett, Raman Chandrasekar, Max Chickering, Panos Ipeirotis, Edith Law, Foster Provost, Anton Mityagin, Luis von Ahn

Human computation is a new research area that studies the process of channeling the vast internet population to perform tasks or provide data towards solving difficult problems that no known computer algorithms can yet solve perfectly and efficiently, e.g. digitize books, recognize objects in images and songs, translate sentences, summarize news articles, annotate videos etc. The goal of HCOMP 2009 is to bring together academic and industry researchers in a stimulating discussion of existing human computation applications, such as Games With A Purpose (e.g. the ESP game), Mechanical Turk and CAPTCHAs, and future directions of this new subject area.

Included in the workshop are invited talks, presentations, posters, and a demo session where participants are invited to showcase their human computation applications.

W8 – Data Mining using Matrices and Tensors (DMMT’09)

Chris Ding, Tao Li

This workshop will present recent advances in algorithms and methods using matrix and scientific computing/applied mathematics for modeling and analyzing massive, high-dimensional, and nonlinear-structured data. One main goal of the workshop is to bring together leading researchers on many topic areas (e.g., computer scientists, computational and applied mathematicians) to assess the state-of-the-art, share ideas, and form collaborations. We also wish to attract practitioners who seek novel ideas for applications.

W9 – Third Workshop on Data Mining Case Studies and Practice Prize (DMCS)

Gabor Melli, Peter van der Putten, Brendan Kitts

The Data Mining Case Studies Workshop and Practice Prize was established to recognize the very best data mining deployments for the year. Data Mining Case Studies will highlight data mining implementations that have been responsible for a significant and measurable improvement in business operations, advanced scientific discoveries, or provided other benefits to humanity. The best paper will be awarded the Practice Prize. Do you have an outstanding data mining application? This is a unique opportunity to be recognized for your work.

W10 – KDD cup 2009: Fast Scoring on a Large Database (KDDcup09)

Isabelle Guyon, David Vogel

This workshop will discuss the results of the KDD cup 2009. The competition is organized around a large dataset provided by the French telecom company Orange. It is a problem of Customer Relationship Management (CRM), a key element of modern marketing strategies. Orange offered the opportunity to work on a large marketing database to predict the propensity of customers to switch provider (churn), buy new products or services (appetency), or buy upgrades or add-ons proposed to them to make the sale more profitable (up-selling).

W11 – The First ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data (U’09)

Jian Pei, Lise Getoor, Ander de Keijzer

The First ACM SIGKDD International Workshop on Knowledge Discovery from Uncertain Data (U’09) is to discuss in depth the challenges, opportunities and techniques on the topic of analyzing and mining uncertain data. The theme of this workshop is to make connections among the research areas of probabilistic databases, probabilistic reasoning, and data mining, as well as to build bridges among the aspects of models, data, applications, novel mining tasks and effective solutions. By making connections among different communities, we aim at understanding each other in terms of scientific foundation as well as commonality and differences in research methodology.

KDD-09 Call For Workshop Proposals (Expired)

The ACM KDD-2009 organizing committee invites proposals for workshops to be held in conjunction with the conference. The purpose of a workshop is to provide participants with the opportunity to present and discuss novel research ideas on active and emerging topics of knowledge discovery and data mining. A workshop should also support the interaction and feedback among topic specialists from academia, industry and government.

A workshop may be organized around industrial applications in a particular domain and the challenges this domain poses, such as the Netflix workshop on recommender systems (http://netflixkddworkshop2008.info/).

A workshop may also include a challenge problem, such as the one on time series classification that took place in 2007 (http://www.cs.ucr.edu/~eamonn/SIGKDD2007TimeSeries.html). A session with papers that address a challenge complements the more diverse sessions with regular papers and improves the potential for discussion. Because such challenges require extra time to plan, we may be willing to provide early notice of acceptance.

The organizers of approved workshops are required to announce the workshop and call for papers, gather submissions, conduct the reviewing process and decide upon the final workshop program. They must also prepare an informal set of workshop proceedings to be distributed with the registration materials at the conference. They may choose to form organizing or program committees for assistance in these tasks. The logistics of the workshops will be done with the help from the ACM KDD-2009 organizers.

source-http://www.kdd.org/kdd/2009/workshops.html

KDD2009: Papers Research and Industrial

Research Papers

A Generalized Co-HITS Algorithm and Its Application to Bipartite Graphs
Hongbo Deng* The Chinese Univ. of Hong Kong; Michael Lyu The Chinese University of Hong Kong; IRWIN KING Chinese University of Hong Kong

A LRT Framework for Fast Spatial Anomaly Detection
Mingxi Wu* Oracle Corporation; Xiuyao Song ; Chris Jermaine University of Florida; Sanjay Ranka University of Florida; John Gums

A Multi-Relational Approach to Spatial Classification
Richard Frank* Simon Fraser University; Martin Ester Simon Fraser University; Arno Knobbe Leiden University

A Principled and Flexible Framework for Finding Alternative Clusterings
ZiJie Qi* UCDavis; Ian Davidson University of California Davis

A Viewpoint-based Approach for Interaction Graph Analysis
Sitaram Asur* Ohio State University; Srinivasan Parthasarathy Ohio State University

Adapting the Right Measures for K-means Clustering
Junjie Wu* Beihang University; Hui Xiong Rutgers University; Jian Chen

An Association Analysis Approach to Biclustering
Gaurav Pandey* University of Minnesota; Gowtham Atluri ; Michael Steinbach University of Minnesota; Chad Myers University of Minnesota; Vipin Kumar University of Minnesota

Analyzing Patterns of User Content Generation in Online Social Networks
Lei Guo* Yahoo!; Enhua Tan Ohio State University; Songqing Chen George Mason University; Xiaodong Zhang Ohio State University; Yihong (Eric) Zhao Yahoo!

Anomalous Window Discovery through Scan Statistics for Linear Intersecting Paths (SSLIP)
Lei Shi University of Maryland Baltimore County; Vandana Janeja* UMBC

Audience Selection for On-line Brand Advertising: Privacy-friendly Social Network Targeting
Foster Provost* NYU; Brian Dalessandro Media6degrees; Rod Hook Coriolis Ventures; Xiaohan Zhang New York University

Augmenting the Generalized Hough Transform to Enable the Mining of Petroglyphs
Qiang Zhu* Univ of California Riverside; Xiaoyue Wang Univ of California Riverside; Eamonn Keogh UC Riverside; Sang-Hee Lee UC Riverside

BBM: Bayesian Browsing Model from Petabyte-scale Data
Chao Liu* Microsoft Research; Fan Guo Carnegie Mellon University; Christos Faloutsos CMU

Cross Domain Distribution Adaptation via Kernel Mapping
Erheng Zhong* Sun Yat-Sen University; Wei Fan IBM T.J.Watson; Jing Peng Montclair State University; Kun Zhang Xavier University of Louisiana; Jiangtao Ren Sun Yat-Sun University; Olivier Verscheure IBM T.J.Watson; Deepak Turaga IBM

Cartesian Contour: A Concise Representation for a Collection of Frequent Sets
Ruoming Jin* Kent State University; Yang Xiang Kent State University; Lin Liu Kent State University

Category Detection Using Hierarchical Mean Shift
Pavan Vatturi Oregon State University; Weng-Keen Wong* Oregon State University

Causality Quantification and Its Applications: Structuring and Modeling of Multivariate Time Series
Takashi Shibuya* The University of Tokyo; Tatsuya Harada The University of Tokyo; Yasuo Kuniyoshi The University of Tokyo

Characteristic Relational Patterns
Arne Koopman* Universiteit Utrecht; Arno Siebes Universiteit Utrecht

Classification of Software Behaviors for Failure Detection: A Discriminative Pattern Mining Approach
David Lo Singapore Management University; Hong Cheng* Chinese University of HongKong; Jiawei Han University of Illinois at Urbana-Champaign; Siau-Cheng Khoo National University of Singapore; Chengnian Sun National University of Singapore

Co-Clustering on Manifolds
Quanquan Gu* Tsinghua University; Jie Zhou Tsinghua University

CoCo: Coding Cost for Parameter-free Outlier Detection

Christian Bohm University of Munich; Katrin Haegler University of Munich; Nikola Muller Max Plank Institute of Biochemistry Martinsried Germany; Claudia Plant* Technische Universitat Munchen

Co-evolution of Social and Affiliation Networks
Hossam Sharara* University of Maryland; Elena Zheleva University of Maryland College Park; Lise Getoor University of Maryland

Collaborative Filtering with Temporal Dynamics
Yehuda Koren* Yahoo! Research

Collective Annotation of Wikipedia Entities in Web Text
Sayali Kulkarni IIT Bombay; Amit Singh IIT Bombay; Ganesh Ramakrishnan IIT Bombay; Soumen Chakrabarti* IIT Bombay

Collusion-Resistant Anonymous Data Collection Method
Mafruz Zaman Ashrafi* Institute For Infocomm Researc; See-Kiong Ng Institute for Infocomm Research

Combining Link and Content for Community Detection: A Discriminative Approach
Tianbao Yang* Michigan State University; Rong Jin Michigan State University; Yun Chi NEC Laboratories America; Shenghuo Zhu NEC Laboratories America Inc.

Connections between the Lines: Augmenting Social Networks with Text
Jonathan Chang* Princeton University; Jordan Boyd-Graber Princeton University; David Blei Princeton University

Consensus Group Based Stable Feature Selection
Lei Yu* Binghamton University; Steven Loscalzo SUNY Binghamton; Chris Ding University of Texas at Arlington

Constant-Factor Approximation Algorithms for Identifying Dynamic Communities
Chayant Tantipathananandh* UIC; Tanya Berger-Wolf UIC

Constrained Optimization for Validation-Guided Conditional Random Field Learning
Minmin Chen ; Yixin Chen* Washington University in St. L

Correlated Itemset Mining in ROC Space: A Constraint Programming Approach
Siegfried Nijssen* Leuven University; Tias Guns Katholieke Universiteit Leuven; Luc De Raedt Katholieke Universiteit Leuven

CP-Summary: A Concise Representation for Browsing Frequent Itemsets
Ardian Poernomo* Nanyang Technological Universi; Vivekanand Gopalkrishnan Nanyang Technological Universi

Detection of Unique Temporal Segments by Information Theoretic Meta-clustering
Shin Ando* Gunma University; Einoshin Suzuki

Differentially-Private Recommender Systems
Frank McSherry* Microsoft Research; Ilya Mironov Microsoft Research

DOULION: Counting Triangles in Massive Graphs with a Coin
Charalampos Tsourakakis* Carnegie Mellon University; U Kang Carnegie Mellon University; Gary Miller Carnegie Mellon University; Christos Faloutsos CMU

Drosophila Gene Expression Pattern Annotation Using Sparse Features and Term-term Interactions
Shuiwang Ji* Arizona State University; Lei Yuan Arizona State University; Ying-Xin Li Nanjing University; Zhi-Hua Zhou Nanjing University; Sudhir Kumar ; Jieping Ye Arizona State University

DynaMMo: Mining and Summarization of Coevolving Sequences with Missing Values
Lei Li* Carnegie Mellon University; Jim McCann Carnegie Mellon University; Nancy Pollard Carnegie Mellon University; Christos Faloutsos CMU

Effective Multi-Label Active Learning for Text Classification
Bishan Yang* Peking University; JianTao Sun ; Zheng Chen

Efficient Anomaly Monitoring Over Moving Object Trajectory Streams
Lei Chen* HKUST; Ada Fu Chinese University of Hong Kong; Yingyi Bu CUHK

Efficient Influence Maximization in Social Networks
Wei Chen* Microsoft Research Asia; Yajun Wang Microsoft Research Asia; Siyu Yang Tsinghua University

Efficient Methods for Topic Model Inference on Streaming Document Collections
Limin Yao* University of Massachusetts Am; David Mimno University of Massachusetts Amherst; Andrew McCallum University of Massachusetts Amherst

Efficiently Learning the Accuracy of Labeling Sources for Selective Sampling
Pinar Donmez* Carnegie Mellon University; Jaime Carbonell Carnegie Mellon University; Jeff Schneider Carnegie Mellon University

Exploiting Wikipedia as External Knowledge for Document Clustering
Tony Hu* Drexel University; Xiaodan Zhang Drexel Univerity; Caimei Lu Drexel University; E.K Park University of Missouri at Kansas City; Xiaohua Zhou Drexel University

Exploring Social Tagging Graph for Web Object Classification
Zhijun Yin* University of Illinois; Rui Li ; Qiaozhu Mei ; Jiawei Han University of Illinois at Urbana-Champaign

Extracting Discriminative Concepts for Domain Adaptation in Text Mining
Bo Chen* CUHK; Wai Lam CUHK; Ivor Tsang NTU; Tak-lam Wong CUHK

Fast Approximate Spectral Clustering
Donghui Yan University of California Berkeley; Ling Huang* Intel Research; Michael Jordan University of California Berkeley

Feature Shaping for Linear SVM Classifiers
George Forman* Hewlett-Packard Labs; Martin Scholz HP Labs; Shyamsundar Rajaram Hewlett-Packard

Finding a Team of Experts in Social Networks
Theodoros Lappas Univ of California Riverside; Kun Liu IBM Almaden; Evimaria Terzi* IBM Almaden

Frequent Pattern Mining with Uncertain Data
Charu Aggarwal* IBM T J Watson Research Center; Yan Li Tsinghua University; Jianyong Wang Tsinghua University; Jing Wang New York University

Genre-based Decomposition of Email Class Noise
Aleksander Kolcz* Microsoft Live Labs; Gordon Cormack University of Waterloo

Grouped Graphical Granger Modeling Methods for Temporal Causal Modeling
Aurelie Lozano* IBM Research; Naoki Abe IBM T J Watson Research Center; Yan Liu IBM Research; Saharon Rosset Tel-Aviv University
Israel

Heterogeneous Source Consensus Learning via Decision Propagation and Negotiation
Jing Gao* UIUC; Wei Fan IBM T.J.Watson; Yizhou Sun ; Jiawei Han University of Illinois at Urbana-Champaign

Improving Clustering Stability with Combinatorial MRFs
Ron Bekkerman* HP Labs; Martin Scholz HP Labs; Krishnamurthy Viswanathan HP Labs

Improving Data Mining Utility with Projective Sampling
Mark Last* BGU

Information Theoretic Regularization for Semi-Supervised Boosting
Lei Zheng Wright State University; Shaojun Wang* Wright State University; Yan Liu Wright State University; Chi-Hoon Lee Yahoo

Issues in Evaluation of Stream Learning Algorithms
Joao Gama* University of Porto; Raquel Sebastiao LIAAD; Pedro Rodrigues LIAAD

Large Human Communication Networks: Patterns and a Utility-Driven Generator
Nan Du* CMU; Christos Faloutsos CMU; Bai Wang ; Leman Akoglu Carnegie Mellon University

Large-Scale Behavioral Targeting
Ye Chen* Yahoo! Labs; Dmitry Pavlov Yahoo! Labs; John Canny Computer Science Division University of California Berkeley

Large-Scale Graph Mining Using Backbone Refinement Classes
Andreas Maunz* Freiburg Center for Data Analy; Christoph Helma in-silico toxicology; Stefan Kramer Institut fur Informatik Technische Universitat Munchen

Large-Scale Sparse Logistic Regression
Jun Liu* Arizona State University; Jianhui Chen ASU; Jieping Ye Arizona State University

Learning Optimal Ranking with Tensor Factorization for Tag Recommendation
Steffen Rendle* University of Hildesheim; Leandro Marinho University of Hildesheim; Alexandros Nanopoulos University of Hildesheim; Lars Schmidt-Thieme University of Hildesheim

Learning Patterns in the Dynamics of Biological Networks
Chang hun You* Washington State University; Lawrence Holder Washington State University; Diane Cook Washington State University

Learning with a Nonexhaustive Training Dataset
Murat Dundar* IUPUI; Arun Bhunia Purdue University; Daniel Hirleman Purdue University; Paul Robinson ; Bartek Rajwa Purdue University

Learning Indexing and Diagnosing Network Faults
Ting Wang* Georgia Tech; Mudhakar Srivatsa IBM T.J. Watson Research Cente; Dakshi Agrawal ; Ling Liu

Measuring the Effects of Preprocessing Decisions and Network Forces in Dynamic Network Analysis
Jerry Scripps* Michigan State University; Pang-Ning Tan Michigan State University; Abdol-Hossein Esfahanian Michigan State University

Meme-tracking and the Dynamics of the News Cycle
Jure Leskovec* Cornell University; Lars Backstrom Cornell University; Jon Kleinberg Cornell University

MetaFac: Community Discovery via Relational Hypergraph Factorization
Yu-Ru Lin* Arizona State University; Jimeng Sun IBM; Paul Castro IBM; Ravi Konuru IBM; Hari Sundaram ; Aisling Kelliher Arizona State University

Mind the Gaps: Weighting the Unknown in Large-Scale One-Class Collaborative Filtering
Rong Pan* HP Labs; Martin Scholz HP Labs

Mining Broad Latent Query Aspects from Search Sessions
Xuanhui Wang UIUC; Deepayan Chakrabarti Yahoo! Research; Kunal Punera* Yahoo! Research

Mining Discrete Patterns via Binary Matrix Factorization
Bao-Hong Shen Arizona State University; Shuiwang Ji Arizona State University; Jieping Ye* Arizona State University

Mining for the Most Certain Predictions from Dyadic Data
Meghana Deodhar* University of Texas at Austin; Joydeep Ghosh The University of Texas at Austin

Mining Rich Session Context to Improve Web Search
Guangyu Zhu* University of Maryland College Park; Gilad Mishne Yahoo! Search and Advertising Sciences

Mining Social Networks for Personalized Email Prioritization
Shinjae Yoo* Carnegie Mellon University; Yiming Yang ; Frank Lin ; Il-Chul Moon

Characterizing Individual Communication Patterns
Dean Malmgren* Northwestern University; Jake Hofman Yahoo! Research; Luis Amaral Northwestern University; Duncan Watts Yahoo! Research

Multi-focal Learning and Its Application to Customer Service Support
Yong Ge* Rutgers University; Hui Xiong Rutgers University; Wenjun Zhou Rutgers University; Ramendra Sahoo IBM T.J. Watson Research Center; Xiaofeng Gao ; Weili Wu

Name-Ethnicity Classification from Open Sources
Anurag Ambekar Stony Brook University; Charles Ward Stony Brook University; Jahangir Mohammed Stony Brook University; Swapna Male Stony Brook University; Steven Skiena* Stony Brook University

New ensemble methods for evolving data streams
Albert Bifet* Universitat Politecnica de Cat; Geoff Holmes University of Waikato; Bernhard Pfahringer University of Waikato Hamilton; Richard Kirkby University of Waikato; Ricard Gavalda Universitat Politecnica de Catalunya

On Burstiness-aware Search for Document Sequences
Theodoros Lappas* Univ of California Riverside; Benjamin Arai Univ of California Riverside; Dimitrios Gunopulos UCR NKUA; Manolis Platakis ; Dimitrios Kotsakos

On Compressing Social Networks
Flavio Chierichetti ; Ravi Kumar* Yahoo; Silvio Lattanzi ; Michael Mitzenmacher ; Alessandro Panconesi ; Prabhakar Raghavan

On the Tradeoff Between Privacy and Utility in Data Publishing
Tiancheng Li* Purdue University; Ninghui Li Purdue University Optimizing Web Traffic via the Media Scheduling Problem Lars Backstrom* Cornell University; Jon Kleinberg Cornell University; Ravi Kumar Yahoo

Parallel Community Detection on Large Networks with Propinquity Dynamics
Yuzhou Zhang* Tsinghua University; Jianyong Wang Tsinghua University; Yi Wang Google Beijing Research; Lizhu Zhou Tsinghua University

Primal Sparse Max-Margin Markov Networks
Jun ZHU* Tsinghua University; Eric Xing Carnegie Mellon Univresity; Bo Zhang Tsinghua University

Probabilistic Frequent Itemset Mining in Uncertain Databases
Matthias Renz* Ludwig-Maximilinas-Universitat; Thomas Bernecker Ludwig-Maximilians-Universitat Munchen; Florian Verhein Ludwig-Maximilians-Universitat Munchen; Andreas Zuefle Ludwig-Maximilians-Universitat Munchen; Hans-Peter Kriegel University of Munich

Quantification and Semi-supervised Classification Methods for Handling Changes in Class Distribution
Jack Chongjie Xue* Fordham University; Gary Weiss Fordham University

Ranking-Based Clustering of Heterogeneous Information Networks with Star Network Schema
Yizhou Sun* UIUC; Yintao Yu UIUC; Jiawei Han University of Illinois at Urbana-Champaign

Regression based Latent Factor Models
Deepak Agarwal* Yahoo!; Bee-Chung Chen Yahoo!

Regret-based Online Ranking for a Growing Digital Library
Erick Delage* Stanford University

Relational Learning via Latent Social Dimensions
Lei Tang* Arizona State University; Huan Liu

Scalable Graph Clustering Using Flows: Applications to Community Discovery
Venu Satuluri The Ohio State University; Srinivasan Parthasarathy* Ohio State University

Scalable Pseudo-Likelihood Estimation in Hybrid Random Fields
Antonino Freno* University of Siena; Edmondo Trentin ; Marco Gori

Social Influence Analysis in Large-scale Networks
Jie Tang* Tsinghua University; Jimeng Sun IBM TJ Watson Research Center; Chi Wang Tsinghua Univ.

Spatial-temporal causal modeling for climate change attribution
Aurelie Lozano* IBM Research; Hongfei Li IBM Research; Alexandru Niculsecu-Mizil IBM Research; Yan Liu IBM Research; Claudia Perlich IBM USA; Jonathan Hosking IBM Research; Naoki Abe IBM T J Watson Research Center

Structured Correspondence Topic Models for Mining Captioned Figures in Biological Literature
Amr Ahmed* Carnegie Mellon Univresity; Eric Xing Carnegie Mellon Univresity; William Cohen Carnegie Mellon Univresity; Robert Murphy Carnegie Mellon Univresity

TANGENT: A Novel, “Surprise-Me”, Recommendation Algorithm
Kensuke Onuma Sony Corporation; Hanghang Tong* CMU; Christos Faloutsos CMU

Tell Me Something I Don’t Know: Randomization Strategies for Iterative Data Mining
Sami Hanhijarvi* Helsinki Univ. of Technology; Markus Ojala Helsinki University of Technology; Niko Vuokko ; Kai Puolamaki ; Nikolaj Tatti Helsinki Univ. of Technology; Heikki Mannila

Temporal Mining for Interactive Workflow Data Analysis
Michele Berlingerio* KDD Lab Pisa ISTI C.N.R.; Fosca Giannotti ISTI CNR; Mirco Nanni KDD Lab – ISTI – CNR; Fabio Pinelli Isti – CNR – Italy Pisa

The Offset Tree for Learning with Partial Labels
John Langford* ; Alina Beygelzimer IBM

Time Series Shapelets: A New Primitive for Data Mining
Lexiang Ye* UC Riverside; Eamonn Keogh UC Riverside

Toward Autonomic Grids: Analyzing the Job Flow with Affinity Streaming
Xiangliang Zhang* INRIA; Cyril Furtlehner ; Julien Perez ; Cecile Germain-Renaud Universite Paris Sud; Michele Sebag Universite Paris-Sud

Towards Efficient Mining of Proportional Fault-Tolerant Frequent Itemsets
Ardian Poernomo* Nanyang Technological Universi; Vivekanand Gopalkrishnan Nanyang Technological Universi

TrustWalker : A Random Walk Model for Combining Trust-based and Item-based Recommendation
Mohsen Jamali* Simon Fraser University; Martin Ester Simon Fraser University

Turning Down the Noise in the Blogosphere
Khalid El-Arini, Carnegie Mellon University; Gaurav Veda; Dafna Shahaf; Carlos Guestrin

User Grouping Behavior in Online Forums
Xiaolin Shi* University of Michigan; Jun ZHU Tsinghua University; Rui Cai Microsoft Research; Lei Zhang Microsoft Research Asia

Using Graph-based Metrics with Empircial Risk Minimization to Speed Up Active Learning on Networked Data
Sofus Macskassy* Fetch Technologies Inc.

WhereNext: a Location Predictor on Trajectory Pattern Mining
Anna Monreale Isti – CNR – Italy Pisa; Fabio Pinelli Isti – CNR – Italy Pisa; Roberto Trasarti* Isti – CNR – Italy Pisa; Fosca Giannotti ISTI CNR

Industrial Papers

A Case Study of Behavior-driven Conjoint Analysis on Yahoo! Front Page Today Module

Wei Chu*, Yahoo! Labs; Seung-Taek Park, Yahoo! Inc.; Todd Beaupre, Yahoo! Inc.; Nitin Motgi, Yahoo! Inc.; Amit Phadke, Yahoo! Inc.; Seinjuti Chakraborty, Yahoo! Inc.; Joe Zachariah, Yahoo! Inc.

Address Standardization with Latent Semantic Association

Honglei Guo*, IBM China Research Lab; Huijia Zhu, IBM China Research Lab; Zhili Guo, IBM China Research Lab; Xiaoxun Zhang, IBM China Research Lab; Zhong Su, IBM China Research Lab

Anonymizing Healthcare Data: A Case Study on the Blood Transfusion Service

Noman Mohammed, Concordia University; Benjamin C. M. Fung*, Concordia University; Patrick C. K. Hung, University of Ontario Institute of Technology; Cheuk-kwong Lee, Hong Kong Red Cross Blood Transfusion Service

Applying Syntactic Similarity Algorithms for Enterprise Information Management

Lucy Cherkasova*, HPLabs; Kave Eshghi, HPLabs; Brad Morrey, HPLabs; Joseph Tucek, HPLabs; Alistair Veitch, HPLabs

Beyond Blacklists: Learning to Detect Malicious Web Sites from Suspicious URLs

Justin Ma*, UC San Diego; Lawrence Saul, UCSD; Stefan Savage, UC San Diego; Geoffrey Voelker, UC San Diego

BGP-lens: Patterns and Anomalies in Internet Routing Updates

B. Aditya Prakash*, Carnegie Mellon University; Nicholas Valler, UCR; David Andersen, CMU; Michalis Faloutsos, UCR; Christos Faloutsos, CMU

Can We Learn a Template-Independent Wrapper for News Article Extraction from a Single Training Site?

Junfeng Wang*, Zhejiang university; Xiaofei He, ; Can Wang, ; Jian Pei, Simon Fraser University; Jiajun Bu, ; Chun Chen, ; Ziyu Guan, ; Wei Vivian Zhang, Microsoft

Catching the Drift: Learning Broad Matches from Clickthrough Data

Sonal Gupta*, University of Texas at Austin; Mikhail Bilenko, Microsoft Research; Matthew Richardson, Microsoft Research Clustering of Event Logs Using Iterative Partitioning Adetokunbo Makanju*, Dalhousie University; Nur Zincir-Heywood, Dalhousie University; Evangelos Milios, Dalhousie University

COA: Finding Novel Patents through Text Analysis

Mohammad Al Hasan*, RPI; W. Scott Spangler, IBM Corporation; Thomas Griffin, IBM Corporation; Alfredo Alba, IBM Corporation

Enabling Analysts in Managed Services for CRM Analytics

Indrajit Bhattacharya, IBM Research; Shantanu Godbole*, IBM Research; Ajay Gupta, IBM Research; Ashish Verma, IBM Research; Jeff Achtermann, IBM MBPS; Kevin English, IBM

Entity Discovery and Assignment for Opinion Mining Applications

Xiaowen Ding*, Univ of Illinois at Chicago; Bing Liu, UIC; Lei Zhang, UIC

Grocery Shopping Recommendations Based on Basket-Sensitive Random Walk

Ming Li*, Unilever UK; Malcolm Dias, Unilever UK; Ian Jarman, Liverpool John Moores University; Wael El-Deredy, University of Manchester; Paulo Lisboa, Liverpool John Moores University

Incorporating Site-Level Knowledge for Incremental Crawling of Web Forums: A List-wise Strategy

Jiang-Ming Yang*, Microsoft Research Asia; Rui Cai, Microsoft Research; Chunsong Wang, University of Wisconsin-Madison; Hua Huang, Beijing University of Posts and Telecommunications; Lei Zhang, Microsoft Research Asia; Wei-Ying Ma, Microsoft Research Asia

Intelligent File Scoring System for Malware Detection from the Gray List

Tao Li*, Florida International University Learning Dynamic Temporal Graphs for Oil-drilling Equipment Monitoring System Yan Liu*, IBM Research; Jayant Kalagnanam

Migration Motif: A Spatial-Temporal Pattern Mining Approach for Financial Markets

Xiaoxi Du, KSU; Ruoming Jin*, Kent State University; Liang Ding, Kent State University; Victor Lee, Kent State University; John Thornton, Kent State University

Mining Brain Region Connectivity for Alzheimer’s Disease Study via Sparse Inverse Covariance Estimation

Liang Sun*, Arizona State University; Rinkal Patel, Arizona State University; Jun Liu, Arizona State University; Kewei Chen, Neuroimaging Banner Alzheimer’s Institute; Teresa Wu, Arizona State University; Jing Li, Arizona State University; Eric Reiman, Banner Alzheimer’s Institute and Banner PET Center; Jieping Ye, Arizona State University

Modeling and Predicting User Behavior in Sponsored Search

Joshua Attenberg*, NYU Polytechnic Institute; Torsten Suel, Yahoo Research; Sandeep Pandey, Yahoo Research

Named Entity Mining from Click-Through Log Using Weakly Supervised Latent Dirichlet Allocation

Gu Xu*, Microsoft Research Asia; Shuang-Hong Yang, Georgia Tech; Hang Li, Microsoft Research Asia

Network Anomaly Detection based on Eigen Equation Compression

Shunsuke Hirose*, NEC Corporation; Kenji Yamanishi, ; Takayuki Nakata, ; Ryohei Fujimaki

OLAP on Search Logs: An Infrastructure Supporting Data-Driven Applications in Search Engines

Bin Zhou, Simon Fraser University; Daxin Jiang*, MSRA; Jian Pei, Simon Fraser University; Hang Li, Microsoft Research Asia

OpinionMiner: A Machine Learning System for Web Opinion Mining and Extraction

Wei Jin*, North Dakota State University; Hung Hay Ho

Pervasive Parallelism in Data Mining: Dataflow solution to Co-clustering Large and Sparse Netflix Data

Srivatsava Daruru, University of Texas at Austin; Nena Marin*, Pervasive Software; Matthew Walker, Pervasive Software; Joydeep Ghosh, The University of Texas at Austin

Predicting Bounce Rates in Sponsored Search Advertisements

D. Sculley*, Google, Inc.; Robert Malkin, Google, Inc; Sugato Basu, Google, Inc; Roberto Bayardo, Google

PSkip: Estimating relevance ranking quality from web search clickthrough data

Kuansan Wang*, Microsoft Research; Toby Walker, ; Zijian Zheng

Query Result Clustering for Object-level Search

Jongwuk Lee, ; Seung-won Hwang*, Postech; Zaiqing Nie, ; Ji-Rong Wen, Microsoft Research Asia

Improving Classification Accuracy Using Automatically Extracted Training Data

Ariel Fuxman*, Microsoft, USA; Anitha Kanna, Microsoft, USA; Andrew Goldberg, University of Wisconsin; Rakesh Agrawal, Microsoft; Panayiotis Tsaparas, Microsoft

Sentiment Analysis of Blogs by Combining Lexical Knowledge with Text Classification

Prem Melville*, IBM; Wojciech Gryc, ; Richard Lawrence, IBM, USA

Seven Pitfalls to Avoid when Running Controlled Experiments on the Web

Thomas Crook, Microsoft; Brian Frasca, Microsoft; Ron Kohavi*, Microsoft; Roger Longbotham, Microsoft

SNARE: A Link Analytic System for Graph Labeling and Risk Detection

Mary McGlohon*, Carnegie Mellon University; Stephen Bay, PricewaterhouseCoopers; Markus Anderle, PricewaterhouseCoopers; David Steier, PricewaterhouseCoopers; Christos Faloutsos, CMU

Sustainable Operation and Management of Data Center Chillers using Temporal Data Mining

Debprakash Patnaik, Virginia Tech; Manish Marwah, HP Labs; Ratnesh Sharma, HP Labs; Naren Ramakrishnan*, Virginia Tech

Towards a Universal Marketplace over the Web: Statistical Multi-label Classification of Service Provider Forms with Simulated Annealing

Kivanc Ozonat*, HP Labs

Towards Combining Web Classification and Web Information Extraction: A Case Study

Ping Luo*, HP Labs China

Source – http://www.kdd.org/kdd/2009/papers.html

KDD 2009 : Demos

Demos

Exploratory Recommender Systems for Sales and Marketing
Michail Vlachos, Abdel Labbi

Open Mobile Miner: A Toolkit for Mobile Data Stream Mining
Shonali Krishnaswamy, Mohamed Medhat Gaber, Marian Harbach, Christian Hugues, Abhijat Sinha, Brett Gillick, Pari Delir Haghighi, and Arkady Zaslavsky

OSD: An Online Web Spam Detection System
Bin Zhou, Jian Pei

Visalix: A Web Application for Visual Data Analysis and Clustering
Loic Lecerf, Boris Chidlovskii

A Flexible Topic-driven Framework for News Exploration
Juanzi Li, Jun Li, and Jie Tang

Model Monitor: Tracking Model Performance in the Real World
Troy Raeder, Nitesh V. Chawla

SHIFTR: A Fast and Scalable System for Ad Hoc Sensemaking of Large Graphs
Duen Horng Chau, Aniket Kittur, Hanghang Tong, Christos Faloutsos, and Jason I. Hong

Curating and Searching the Annotated Web
Amit Singh, Sayali Kulkarni, Somnath Banerjee, Ganesh Ramakrishnan, Soumen Chakrabarti

Expert2Bólè: From Expert Finding to Bólè Search
Zi Yang, Jie Tang, Bo Wang, Jingyi Guo, and Juanzi

Spam Miner: A Platform for Detecting and Characterizing Spam Campaigns
Pedro H. Calais Guerra, Douglas E. V. Pires, Dorgival Guedes, Wagner Meira Jr., Cristine Hoepers, Klaus Steding-Jessen