Theory

• 
  Alan Herschtal, Bhavani Raskutti, Peter K. Campbell: Area Under ROC Optimisation using a Ramp Approximation.
• 
  Chih-Ming Hsu, Ming-Syan Chen: On the Necessary and Sufficient Conditions of a Meaningful Distance Function for High Dimensional Data Space.
• 
  Jieping Ye, Tao Xiong, Ravi Janardan: CPM: A Covariance-preserving Projection Method.
• 
  Edwin P. D. Pednault: Transform Regression and the Kolmogorov Superposition Theorem.

Enterprise Applications

• 
  Indrajit Bhattacharya, Lise Getoor: A Latent Dirichlet Model for Unsupervised Entity Resolution.
• 
  Sheng Zhang, James Ford, Fillia Makedon: Deriving Private Information from Randomly Perturbed Ratings.
• 
  Christopher P. Diehl, Lise Getoor, Galileo Namata: Name Reference Resolution in Organizational Email Archives.
• 
  Michael C. Burl, Dennis DeCoste, Brian L. Enke, Dominic Mazzoni, William J. Merline, Lucas Scharenbroich: Automated Knowledge Discovery from Simulators.

Anomalies and Outliers

• 
  Pei Sun, Sanjay Chawla, Bavani Arunasalam: Mining for Outliers in Sequential Databases.
• 
  Chao Liu, Xifeng Yan, Jiawei Han: Mining Control Flow Abnormality for Logic Error Isolation.
• 
  György J. Simon, Hui Xiong, Eric Eilertson, Vipin Kumar: Scan Detection: A Data Mining Approach.

Network Relations

• 
  Carsten Riggelsen: Learning Bayesian Networks from Incomplete Data: An Efficient Method for Generating Approximate Predictive Distributions.
• 
  Facundo Bromberg, Dimitris Margaritis, Vasant Honavar: Efficient Markov Network Structure Discovery using Independence Tests.
• 
  Souptik Datta, Chris Giannella, Hillol Kargupta: K-Means Clustering Over a Large, Dynamic Network.

Prototype Generation

• 
  Benjamin J. Anderson, Deborah S. Gross, David R. Musicant, Anna M. Ritz, Thomas G. Smith, Leah E. Steinberg: Adapting K-Medians to Generate Normalized Cluster Centers.
• 
  Hans-Peter Kriegel, Matthias Schubert: Advanced Prototype Machines: Exploring Prototypes for Classification.
• 
  Andrea Tagarelli, Sergio Greco: Toward Semantic XML Clustering. 188-199

Applications in Biology

• 
  Xiaohua Hu, Xiaodan Zhang, Illhoi Yoo, Yanqing Zhang: A Semantic Approach for Mining Hidden Links from Complementary and Non-interactive Biomedical Literature.
• 
  Charu C. Aggarwal: Representation is Everything: Towards Efficient and Adaptable Similarity Measures for Biological Data.
• 
  Sen Zhang, Jason Tsong-Li Wang: Mining Frequent Agreement Subtrees in Phylogenetic Databases.

Clustering

• 
  Zhijie Chen, Weizhen Chen, Qile Chen, Mian-Yun Chen: Trend Relational Analysis and Grey-Fuzzy Clustering Method.
• 
  Martin Ester, Rong Ge, Byron J. Gao, Zengjian Hu, Boaz Ben-Moshe: Joint Cluster Analysis of Attribute Data and Relationship Data: the Connected k-Center Problem.
• 
  Muna Al-Razgan, Carlotta Domeniconi: Weighted Clustering Ensembles.
• 
  Jerry Scripps, Pang-Ning Tan: Clustering in the Presence of Bridge-Nodes.

Pattern Mining

• 
  Hongyan Liu, Jiawei Han, Dong Xin, Zheng Shao: Mining Interesting Patterns from Very High Dimensional Data: A Top-Down Row Enumeration Approach.
• 
  Jianwei Li, Alok N. Choudhary, Nan Jiang, Wei-keng Liao: Mining Frequent Patterns by Differential Refinement of Clustered Bitmaps.
• 
  Jin Soung Yoo, Shashi Shekhar, Sangho Kim, Mete Celik: Discovery of Co-evoluting Spatial Co-located Event Sets.
• 
  Evimaria Terzi, Panayiotis Tsaparas: Efficient Algorithms for Sequence Segmentation.

Temporal Data and Random Walks

• 
  Feng Cao, Martin Ester, Weining Qian, Aoying Zhou: Density-Based Clustering over an Evolving Data Stream with Noise.
• 
  Yunpeng Xu, Xing Yi, Changshui Zhang: A Random Walks Method for Text Classification.
• 
  Fosca Giannotti, Mirco Nanni, Dino Pedreschi: Efficient Mining of Temporally Annotated Sequences.

Dimension Reduction and Coupling

• 
  Charu C. Aggarwal: A Framework for Local Supervised Dimensionality Reduction of High Dimensional Data.
• 
  Ella Bingham, Aristides Gionis, Niina Haiminen, Heli Hiisilä, Heikki Mannila, Evimaria Terzi: Segmentation and dimensionality reduction.
• 
  Juan K. Lin: Probabilistic Multi-State Split-Merge Algorithm for Coupling Parameter Estimates.

Item Sets

• 
  Arno Siebes, Jilles Vreeken, Matthijs van Leeuwen: Item Sets that Compress.
• 
  Jinze Liu, Susan Paulsen, Xing Sun, Wei Wang, Andrew B. Nobel, Jan Prins: Mining Approximate Frequent Itemsets In the Presence of Noise: Algorithm and Analysis.
• 
  Claudio Lucchese, Salvatore Orlando, Raffaele Perego: Mining frequent closed itemsets out-of-core.

Collaborative Mining

• 
  Ran Wolff, Kanishka Bhaduri, Hillol Kargupta: Local L2-Thresholding Based Data Mining in Peer-to-Peer Systems.
• 
  Tak-Lam Wong, Wai Lam, Shing-Kit Chan: Collaborative Information Extraction and Mining from Multiple Web Documents.
• 
  Khaled M. Hammouda, Mohamed S. Kamel: Collaborative Document Clustering.

• 
  Byron J. Gao, Martin Ester: Cluster Description Formats, Problems and Algorithms.
• 
  Guimei Liu, Jinyan Li, Limsoon Wong, Wynne Hsu: Positive Borders or Negative Borders: How to Make Lossless Generator Based Representations Concise.
• 
  Max Welling, Kenichi Kurihara: Bayesian K-Means as a "Maximization-Expectation" Algorithm.
• 
  Charu C. Aggarwal, Philip S. Yu: A Framework for Clustering Massive Text and Categorical Data Streams.
• 
  Sei-Hyung Lee, Karen M. Daniels: Cone Cluster Labeling for Support Vector Clustering.
• 
  Jing Gao, Pang-Ning Tan, Haibin Cheng: Semi-Supervised Clustering with Partial Background Information.
• 
  Geetha Jagannathan, Krishnan Pillaipakkamnatt, Rebecca N. Wright: A New Privacy-Preserving Distributed k-Clustering Algorithm.
• 
  Pedro Pereira Rodrigues, João Gama, João Pedro Pedroso: ODAC: Hierarchical Clustering of Time Series Data Streams.
• 
  Keke Chen, Ling Liu: Detecting the Change of Clustering Structure in Categorical Data Streams.
• 
  Matthew Eric Otey, Srinivasan Parthasarathy, Donald C. Trost: Dissimilarity Measures for Detecting Hepatotoxicity in Clinical Trial Data.
• 
  Sreangsu Acharyya: Transductive De-Noising and Dimensionality Reduction using Total Bregman Regression.
• 
  Yu Fujimoto, Noboru Murata: Robust Estimation for Mixture of Probability Tables based on beta-likelihood.
• 
  Vikas C. Raykar, Ramani Duraiswami: Fast optimal bandwidth selection for kernel density estimation.
• 
  Hisashi Kashima: Risk-Sensitive Learning via Expected Shortfall Minimization.
• 
  Dongwei Cao, Daniel Boley: On Approximate Solutions to Support Vector Machines.
• 
  Eugene Agichtein: Confidence Estimation Methods for Partially Supervised Information Extraction.
• 
  Jacek P. Kukluk, Lawrence B. Holder, Diane J. Cook: Inference of Node Replacement Recursive Graph Grammars.
• 
  Sheng Zhang, Weihong Wang, James Ford, Fillia Makedon: Learning from Incomplete Ratings Using Non-negative Matrix Factorization.
• 
  Yi Fang, Hyun-Woo Cho, Myong Kee Jeong: Health monitoring of a shaft transmission system via hybrid models of PCR and PLS.
• 
  Xiaodan Song, Ching-Yung Lin, Belle L. Tseng, Ming-Ting Sun: Modeling Evolutionary Behaviors for Community-based Dynamic Recommendation.
• 
  Binyamin Rosenfeld, Ronen Feldman, Moshe Fresko: A Systematic Cross-Comparison of Sequence Classifiers.
• 
  Saharon Rosset, Richard D. Lawrence: Data-Enhanced Predictive Modeling for Sales Targeting.
• 
  Abraham Bagherjeiran, Chandrika Kamath: Graph-based Methods for Orbit Classification.
• 
  Olfa Nasraoui, Suchandra Goswami: Mining and Validating Localized Frequent Itemsets with Dynamic Tolerance.
• 
  Saikat Mukherjee, Chang Zhao, I. V. Ramakrishnan: Profiling Protein Families from Partially Aligned Sequences.
• 
  Xin Chen, Yi-Fang Wu: Personalized Knowledge Discovery: Mining Novel Association Rules from Text.
• 
  Jing Gao, Haibin Cheng, Pang-Ning Tan: A Novel Framework for Incorporating Labeled Examples into Anomaly Detection.
• 
  Anthony J. Bonner, Han Liu: Towards the Prediction of Protein Abundance from Tandem Mass Spectrometry Data.
• 
  Mehmet M. Dalkilic, Wyatt T. Clark, James C. Costello, Predrag Radivojac: Using Compression to Identify Classes of Inauthentic Texts.
• 
  Amol Ghoting, Srinivasan Parthasarathy, Matthew Eric Otey: Fast Mining of Distance-Based Outliers in High Dimensional Datasets.
• 
  Yufeng Kou, Chang-Tien Lu, Dechang Chen: Spatial Weighted Outlier Detection.
• 
  Olfa Nasraoui, Carlos Rojas: Robust Clustering for Tracking Noisy Evolving Data Streams.
• 
  Unil Yun, John J. Leggett: WIP: mining Weighted Interesting Patterns with a strong weight and/or support affinity.
• 
  Mark Cheng-Enn Hsieh, Yi-Hung Wu, Arbee L. P. Chen: Discovering Frequent Tree Patterns over Data Streams.
• 
  Yan Huang, Liqin Zhang, Pusheng Zhang: Finding Sequential Patterns from a Massive Number of Spatio-Temporal Events.
• 
  Roger Ming Hieng Ting, James Bailey: Mining Minimal Contrast Subgraph Patterns.

last updated on Thu May 23 03:41:34 2013 by the dblp team
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