Online mining for association rules and collective anomalies in data streams.
Shaaban AbbadyCheng-Yuan KeJennifer LavergneJian ChenVijay V. RaghavanRyan BentonPublished in: IEEE BigData (2017)
Keyphrases
- data streams
- association rules
- itemsets
- frequent itemsets
- market basket data
- association rule mining
- mining algorithm
- mining association rules
- knowledge discovery
- frequent closed itemsets
- apriori algorithm
- association mining
- data mining
- continuous data streams
- stream mining
- weighted association rules
- quantitative association rules
- interesting association rules
- association rules mining
- frequent patterns
- data mining techniques
- mining data streams
- mining frequent itemsets
- sliding window
- anomaly detection
- transactional databases
- multiple data streams
- data stream mining
- interestingness measures
- online learning
- frequent itemset mining
- discovering association rules
- market basket analysis
- pattern mining
- closed frequent itemsets
- transaction data
- rule mining
- formal concept analysis
- emerging patterns
- high speed data streams
- algorithm for mining association rules
- sensor networks
- frequent pattern mining
- streaming data
- association rule discovery
- association patterns
- discovered knowledge
- frequent sets
- interesting patterns
- concept drift
- minimum support and minimum confidence
- stream data
- continuous queries
- data mining methods
- sequential patterns
- data mining algorithms
- association rule mining algorithm
- closed itemsets
- web mining
- stream data mining
- data sets