icdm 2007 论文列表Proceedings of the 7th IEEE International Conference on Data Mining (ICDM 2007), October 28-31, 2007, Omaha, Nebraska, USA. |
Lazy Bagging for Classifying Imbalanced Data.
Active Learning from Data Streams.
Efficient Discovery of Frequent Approximate Sequential Patterns.
Discovering Temporal Communities from Social Network Documents.
Co-ranking Authors and Documents in a Heterogeneous Network.
Noise Modeling with Associative Corruption Rules.
Incremental Subspace Clustering over Multiple Data Streams.
Cocktail Ensemble for Regression.
Locally Constrained Support Vector Clustering.
estMax: Tracing Maximal Frequent Itemsets over Online Data Streams.
Mechanism Design for Clustering Aggregation by Selfish Systems.
Topical N-Grams: Phrase and Topic Discovery, with an Application to Information Retrieval.
Transitional Patterns and Their Significant Milestones.
Preserving Privacy through Data Generation.
Using Significant, Positively Associated and Relatively Class Correlated Rules for Associative Classification of Imbalanced Datasets.
A Novel Criterion for Onset Detection: Differential Information Redundancy with Application to Human Movement Initiation.
Statistical Learning Algorithm for Tree Similarity.
Understanding Discrete Classifiers with a Case Study in Gene Prediction.
A Support Vector Approach to Censored Targets.
Exploration of Link Structure and Community-Based Node Roles in Network Analysis.
An Efficient Spectral Algorithm for Network Community Discovery and Its Applications to Biological and Social Networks.
Can the Content of Public News Be Used to Forecast Abnormal Stock Market Behaviour?
Sampling for Sequential Pattern Mining: From Static Databases to Data Streams.
Local Word Bag Model for Text Categorization.
Weighted Additive Criterion for Linear Dimension Reduction.
High-Speed Function Approximation.
Consensus Clusterings.
Detecting Subdimensional Motifs: An Efficient Algorithm for Generalized Multivariate Pattern Discovery.
Optimizing Frequency Queries for Data Mining Applications.
A Text Classification Framework with a Local Feature Ranking for Learning Social Networks.
Failure Prediction in IBM BlueGene/L Event Logs.
Solving Consensus and Semi-supervised Clustering Problems Using Nonnegative Matrix Factorization.
Connections between Mining Frequent Itemsets and Learning Generative Models.
Optimal Subsequence Bijection.
Change-Point Detection in Time-Series Data Based on Subspace Identification.
A Computational Approach to Style in American Poetry.
Analyzing and Detecting Review Spam.
Trend Motif: A Graph Mining Approach for Analysis of Dynamic Complex Networks.
Web Site Recommendation Using HTTP Traffic.
On Meta-Learning Rule Learning Heuristics.
Computing Correlation Anomaly Scores Using Stochastic Nearest Neighbors.
Semi-supervised Document Clustering via Active Learning with Pairwise Constraints.
Training Conditional Random Fields by Periodic Step Size Adaptation for Large-Scale Text Mining.
Confident Identification of Relevant Objects Based on Nonlinear Rescaling Method and Transductive Inference.
Bayesian Folding-In with Dirichlet Kernels for PLSI.
Using Burstiness to Improve Clustering of Topics in News Streams.
Prism: A Primal-Encoding Approach for Frequent Sequence Mining.
Cross-Mining Binary and Numerical Attributes.
Mining Interpretable Human Strategies: A Case Study.
Extracting Product Comparisons from Discussion Boards.
Bandit-Based Algorithms for Budgeted Learning.
Recommendation via Query Centered Random Walk on K-Partite Graph.
Document Transformation for Multi-label Feature Selection in Text Categorization.
gApprox: Mining Frequent Approximate Patterns from a Massive Network.
Predicting Blogging Behavior Using Temporal and Social Networks.
Zonal Co-location Pattern Discovery with Dynamic Parameters.
Efficient Kernel Discriminant Analysis via Spectral Regression.
Latent Dirichlet Conditional Naive-Bayes Models.
Finding Predictive Runs with LAPS.
DUSC: Dimensionality Unbiased Subspace Clustering.
A Semantic Kernel for Semi-structured DocumentS.
Binary Matrix Factorization with Applications.
Disk Aware Discord Discovery: Finding Unusual Time Series in Terabyte Sized Datasets.
Multilevel Belief Propagation for Fast Inference on Markov Random Fields.
A Generalization of Proximity Functions for K-Means.
Structure-Based Statistical Features and Multivariate Time Series Clustering.
Language-Independent Set Expansion of Named Entities Using the Web.
Improving Text Classification by Using Encyclopedia Knowledge.
Local Probabilistic Models for Link Prediction.
Maximum Entropy Based Significance of Itemsets.
General Averaged Divergence Analysis.
Social Network Extraction of Academic Researchers.
Lightweight Distributed Trust Propagation.
Mining Statistical Information of Frequent Fault-Tolerant Patterns in Transactional Databases.
Sample Selection for Maximal Diversity.
Supervised Learning by Training on Aggregate Outputs.
Parallel Mining of Frequent Closed Patterns: Harnessing Modern Computer Architectures.
Community Learning by Graph Approximation.
A Pairwise Covariance-Preserving Projection Method for Dimension Reduction.
Succinct Matrix Approximation and Efficient k-NN Classification.
Finding Cohesive Clusters for Analyzing Knowledge Communities.
Improving Knowledge Discovery in Document Collections through Combining Text Retrieval and Link Analysis Techniques.
Data Discretization Unification.
Dynamic Micro Targeting: Fitness-Based Approach to Predicting Individual Preferences.
Efficient Algorithms for Mining Significant Substructures in Graphs with Quality Guarantees.
ORIGAMI: Mining Representative Orthogonal Graph Patterns.
On Appropriate Assumptions to Mine Data Streams: Analysis and Practice.
Non-redundant Multi-view Clustering via Orthogonalization.
Detecting Fractures in Classifier Performance.
Depth-Based Novelty Detection and Its Application to Taxonomic Research.
Incorporating User Provided Constraints into Document Clustering.
A Cascaded Approach to Biomedical Named Entity Recognition Using a Unified Model.
Mining Frequent Itemsets in a Stream.
Spectral Regression: A Unified Approach for Sparse Subspace Learning.
The Chosen Few: On Identifying Valuable Patterns.
Rule Cubes for Causal Investigations.
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights.
Temporal Analysis of Semantic Graphs Using ASALSAN.
Efficient Data Sampling in Heterogeneous Peer-to-Peer Networks.
Clustering Needles in a Haystack: An Information Theoretic Analysis of Minority and Outlier Detection.
How Much Noise Is Too Much: A Study in Automatic Text Classification.