Special issue on “context-aware data mining (CADM)”
COAT: COnstraint-based anonymization of transactions
CAS-Mine: providing personalized services in context-aware applications by means of generalized rules
Discovering frequent behaviors: time is an essential element of the context
Sensor data analysis for equipment monitoring
Statistical semantics for enhancing document clustering
On ontology-driven document clustering using core semantic features
An efficient graph-mining method for complicated and noisy data with real-world applications
An AHP-based approach toward enterprise architecture analysis based on enterprise architecture quality attributes
Fuzzy emerging patterns for classifying hard domains
Improving SVM classification on imbalanced time series data sets with ghost points
Generalized sparse metric learning with relative comparisons
The k-anonymity and l-diversity approaches for privacy preservation in social networks against neighborhood attacks
Single pass text classification by direct feature weighting
Composite kernels for semi-supervised clustering
Clustering uncertain trajectories
Record-level peculiarity-based data analysis and classifications
Generational analysis of tension and entropy in data structures: impact on automatic data integration and on the semantic web
Banded structure in binary matrices
Distance approximation techniques to reduce the dimensionality for multimedia databases
Data warehousing and knowledge discovery from sensors and streams
Fast and memory efficient mining of high-utility itemsets from data streams: with and without negative item profits
One-class learning and concept summarization for data streams
Recommending items in pervasive scenarios: models and experimental analysis
Energy conservation in wireless sensor networks: a rule-based approach
Energy-saving models for wireless sensor networks
On link privacy in randomizing social networks
SimClus: an effective algorithm for clustering with a lower bound on similarity
An integration of fuzzy association rules and WordNet for document clustering
COID: A cluster–outlier iterative detection approach to multi-dimensional data analysis
