SIGKDD(KDD) 2010 论文列表
Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, July 25-28, 2010.
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The next generation of transportation systems, greenhouse emissions, and data mining.
Transfer metric learning by learning task relationships.
Multi-task learning for boosting with application to web search ranking.
Learning incoherent sparse and low-rank patterns from multiple tasks.
Nonnegative shared subspace learning and its application to social media retrieval.
Unsupervised transfer classification: application to text categorization.
Universal multi-dimensional scaling.
Semi-supervised sparse metric learning using alternating linearization optimization.
Scalable similarity search with optimized kernel hashing.
Compressed fisher linear discriminant analysis: classification of randomly projected data.
An efficient causal discovery algorithm for linear models.
Mining periodic behaviors for moving objects.
Online discovery and maintenance of time series motifs.
Evolutionary hierarchical dirichlet processes for multiple correlated time-varying corpora.
GLS-SOD: a generalized local statistical approach for spatial outlier detection.
Finding effectors in social networks.
Social action tracking via noise tolerant time-varying factor graphs.
Community-based greedy algorithm for mining top-K influential nodes in mobile social networks.
Scalable influence maximization for prevalent viral marketing in large-scale social networks.
Inferring networks of diffusion and influence.
DivRank: the interplay of prestige and diversity in information networks.
Multi-label learning by exploiting label dependency.
Mass estimation and its applications.
Combined regression and ranking.
BioSnowball: automated population of Wikis.
A probabilistic model for personalized tag prediction.
Growing a tree in the forest: constructing folksonomies by integrating structured metadata.
The community-search problem and how to plan a successful cocktail party.
PET: a statistical model for popular events tracking in social communities.
Towards mobility-based clustering.
Mixture models for learning low-dimensional roles in high-dimensional data.
An energy-efficient mobile recommender system.
Trust network inference for online rating data using generative models.
Fast query execution for retrieval models based on path-constrained random walks.
Ensemble pruning via individual contribution ordering.
Direct mining of discriminative patterns for classifying uncertain data.
Designing efficient cascaded classifiers: tradeoff between accuracy and cost.
Class-specific error bounds for ensemble classifiers.
Large linear classification when data cannot fit in memory.
Redefining class definitions using constraint-based clustering: an application to remote sensing of the earth's surface.
On community outliers and their efficient detection in information networks.
Modeling relational events via latent classes.
Semi-supervised feature selection for graph classification.
Latent aspect rating analysis on review text data: a rating regression approach.
Semantic relation extraction with kernels over typed dependency trees.
Document clustering via dirichlet process mixture model with feature selection.
Mining positive and negative patterns for relevance feature discovery.
Learning to combine discriminative classifiers: confidence based.
Generative models for ticket resolution in expert networks.
Temporal recommendation on graphs via long- and short-term preference fusion.
Training and testing of recommender systems on data missing not at random.
Fast online learning through offline initialization for time-sensitive recommendation.
Combining predictions for accurate recommender systems.
The topic-perspective model for social tagging systems.
Topic models with power-law using Pitman-Yor process.
Online multiscale dynamic topic models.
Discriminative topic modeling based on manifold learning.
Boosting with structure information in the functional space: an application to graph classification.
Discovering frequent subgraphs over uncertain graph databases under probabilistic semantics.
Connecting the dots between news articles.
Mining program workflow from interleaved traces.
Fast euclidean minimum spanning tree: algorithm, analysis, and applications.
Unifying dependent clustering and disparate clustering for non-homogeneous data.
Clustering by synchronization.
A hierarchical information theoretic technique for the discovery of non linear alternative clusterings.
Flexible constrained spectral clustering.
Dynamics of conversations.
Parallel SimRank computation on large graphs with iterative aggregation.
Neighbor query friendly compression of social networks.
Balanced allocation with succinct representation.
Fast nearest-neighbor search in disk-resident graphs.
Discovering frequent patterns in sensitive data.
Data mining with differential privacy.
Collusion-resistant privacy-preserving data mining.
k-Support anonymity based on pseudo taxonomy for outsourcing of frequent itemset mining.
Negative correlations in collaboration: concepts and algorithms.
Extracting temporal signatures for comprehending systems biology models.
Topic dynamics: an alternative model of bursts in streams of topics.
Discovering significant relaxed order-preserving submatrices.
Why label when you can search?: alternatives to active learning for applying human resources to build classification models under extreme class imbalance.
The new iris data: modular data generators.
Learning with cost intervals.
Cold start link prediction.
DUST: a generalized notion of similarity between uncertain time series.
On the quality of inferring interests from social neighbors.
Privacy-preserving outsourcing support vector machines with random transformation.
Versatile publishing for privacy preservation.
Feature selection for support vector regression using probabilistic prediction.
Unsupervised feature selection for multi-cluster data.
An efficient algorithm for a class of fused lasso problems.
A scalable two-stage approach for a class of dimensionality reduction techniques.
Grafting-light: fast, incremental feature selection and structure learning of Markov random fields.
Probably the best itemsets.
Mining top-k frequent items in a data stream with flexible sliding windows.
Mining uncertain data with probabilistic guarantees.
Frequent regular itemset mining.
UP-Growth: an efficient algorithm for high utility itemset mining.
New perspectives and methods in link prediction.
Suggesting friends using the implicit social graph.
User browsing models: relevance versus examination.
Estimating rates of rare events with multiple hierarchies through scalable log-linear models.
Mining advisor-advisee relationships from research publication networks.
Medical coding classification by leveraging inter-code relationships.
An integrated machine learning approach to stroke prediction.
Active learning for biomedical citation screening.
Metric forensics: a multi-level approach for mining volatile graphs.
TIARA: a visual exploratory text analytic system.
Malstone: towards a benchmark for analytics on large data clouds.
Tropical cyclone event sequence similarity search via dimensionality reduction and metric learning.
Using data mining techniques to address critical information exchange needs in disaster affected public-private networks.
Diagnosing memory leaks using graph mining on heap dumps.
Beyond heuristics: learning to classify vulnerabilities and predict exploits.
Automatic malware categorization using cluster ensemble.
Detecting abnormal coupled sequences and sequence changes in group-based manipulative trading behaviors.
Optimizing debt collections using constrained reinforcement learning.
Data mining to predict and prevent errors in health insurance claims processing.
Discovery of significant emerging trends.
Multiple kernel learning for heterogeneous anomaly detection: algorithm and aviation safety case study.
MineFleet®: an overview of a widely adopted distributed vehicle performance data mining system.
Exploitation and exploration in a performance based contextual advertising system.
Overlapping experiment infrastructure: more, better, faster experimentation.
Evaluating online ad campaigns in a pipeline: causal models at scale.
The quantification of advertising: (+ lessons from building businesses based on large scale data mining).
Data winnowing.
Data mining in the online services industry.
