Graph mining for discovering infrastructure patterns in configuration management databases
Analyzing collective behavior from blogs using swarm intelligence
Threshold conditions for arbitrary cascade models on arbitrary networks
Efficient algorithms for influence maximization in social networks
Algorithms for mining the evolution of conserved relational states in dynamic networks
Wrapper positive Bayesian network classifiers
Mining frequent conjunctive queries in relational databases through dependency discovery
Structuring persistent chat conversations: experimental results of the chatsistance tool
Novel approaches to crawling important pages early
SMOTE-RSB
*: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets theory
Dynamic classifier ensemble for positive unlabeled text stream classification
Density-based weighting multi-surface least squares classification with its applications
Parsimonious unsupervised and semi-supervised domain adaptation with good similarity functions
A countably infinite mixture model for clustering and feature selection
MDL-based time series clustering
Restoring coverage to the Bayesian false discovery rate control procedure
Spatial co-location pattern discovery without thresholds
Diverse dimension decomposition for itemset spaces
Batch incremental processing for FP-tree construction using FP-Growth algorithm
Data preprocessing techniques for classification without discrimination
Data classification through an evolutionary approach based on multiple criteria
Exponential family tensor factorization: an online extension and applications
Web user clustering and Web prefetching using Random Indexing with weight functions
Online active multi-field learning for efficient email spam filtering
From global to local and viceversa: uses of associative rule learning for classification in imprecise environments
A software trustworthiness evaluation model using objective weight based evidential reasoning approach
Compression and aggregation of Bayesian estimates for data intensive computing
Facing the reality of data stream classification: coping with scarcity of labeled data
