Suggested papers

Classification

• Thorsten Joachims, A Support Vector Method for Multivariate Performance Measure

Clustering/Co-clustering

• Tao Li, A General Model for Clustering Binary Data
• I. Dhillon, Y. Guan, and B. Kulis, Kernel K-means, Spectral Clustering and Normalized Cuts

Data Preprocessing (Text/Web Oriented)

• Jun Yan, Ning Liu, Benyu Zhang, Shuicheng Yan, Zheng Chen, Qian Sheng Cheng, Weiguo Fan, Wei-Ying Ma, OCFS: Optimal Orthogonal Centroid Feature Selection for Text Categorization

Data Preprocessing (Micro-Array Oriented)

• Xiaofei He, Deng Cai, Partha Niyogi, Laplacian Score for Feature Selection

Ensemble Learning

• Saharon Rosset, Robust Boosting and Its Relation to Bagging

Association Rules /Bioinformatics

• Elisabeth Georgii, Lothar Richter, Ulrich Rückert, Stefan Kramer, Analyzing microarray data using quantitative association rules

Semi-supervised Classification/Clustering, One-class Learning

• Brian Kulis, Sugato Basu, Inderjit Dhillon, Raymond Mooney, Semi-Supervised Graph Clustering: A Kernel Approach

Mining Semi-Structured Data

• Hashimoto, K., Aoki-Kinoshita, K. F., Ueda, N., Kanehisa, M., and Mamitsuka, H. A new efficient probabilistic model for mining labeled ordered trees

Web Mining:

• Sun, J.; Zeng, H.; Liu, H.; Lu, Y. & Chen, Z. CubeSVD: A novel approach to personalized web search

Groups

Clustering

• Cross-Relational Clustering with User's Guidance ( KDD'05 ) - Xiaoxin Yin, Jiawei Han, Philip S. Yu
• A method for personalized clustering in data intensive web applications
• A general model for clustering binary data
• An Investigation of Computational and Informational Limits in Gaussian Mixture Clustering
• Bayesian hierarchical clustering
• Kernel kmeans, Spectral Clustering and Normalized Cuts

Data Preprocessing

• An Evaluation on Feature Selection for Text Clustering

Ensemble Learning

• Robust Boosting and its Relation to Bagging
• Margin Maximizing Loss Functions

Association Rules /Bioinformatics

• Quantitative Associaton Rules Based on Half-Spaces: An Optimization Approach
• Mining gene expression databases for association rules
• Handling very large numbers of association rules in the analysis of microarray data
• Growing Bayesian network models of gene networks from seed genes

Semi-supervised Classification/Clustering, one-class Learning

• Integrating constraints and metric learning in semi-supervised clustering
• Semi-supervised Graph Clustering: A Kernel Approach

Web Mining

• Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data
• Evaluation of web usage mining approaches for user's next request prediction
• Web Usage Mining Based on Probabilistic Latent Semantic Analysis
• On improving local website search using web server traffic logs: a preliminary report
• FS-Miner: Efficient and Incremental Mining of Frequent Sequence Patterns in Web logs
• Identifying Best Bet web search results by Mining past User Behavior
• Ontology-based Web mining model: representations of user profiles
• Personalized Web Search by Mapping User Queries to Categories
• SEWeP: using site semantics and a taxonomy to enhance the Web personalization process
• Privacy Preserving Web Recommender System
• A Maximum Entropy Web Recommendation System: Combining Collaborative and Content Features
• LinkSelector: A Web Mining Approach to Hyperlink Selection for Web Portals
• Automatic Identification of Informative Sections of Web Pages
• A comparison of implicit and explicit links for web page classification
• Intelligent web information access: An intelligent search agent system for semantic information retrieval on the internet
• Finding advertising keywords on web pages
• Rule Assisted Prefetching (RAP) in web server caching.
• Applications of web mining– from web search engine to P2P filtering
• Web mining - a key enabler in e-business

Others

• Mining Frequent Patterns Over Data Streams
• Canonical Correlation Analysis – An overview with application to learning methods
• Visual Data Mining using Principled Projection Algorithms and Information Visualization Techniques
• Similarity Measures for Image Mining