Efficient mining of association rules using closed itemset lattices
Information Systems
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Computing iceberg concept lattices with TITANIC
Data & Knowledge Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Finding recent frequent itemsets adaptively over online data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
CFI-Stream: mining closed frequent itemsets in data streams
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Catch the moment: maintaining closed frequent itemsets over a data stream sliding window
Knowledge and Information Systems
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
An on-line interactive method for finding association rules data streams
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
A survey on algorithms for mining frequent itemsets over data streams
Knowledge and Information Systems
Incremental updates of closed frequent itemsets over continuous data streams
Expert Systems with Applications: An International Journal
Efficient Vertical Mining of Frequent Closures and Generators
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
A study on interestingness measures for associative classifiers
Proceedings of the 2010 ACM Symposium on Applied Computing
Estimating missing data in data streams
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Mining closed itemsets in data stream using formal concept analysis
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Multi-objective PSO algorithm for mining numerical association rules without a priori discretization
Expert Systems with Applications: An International Journal
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Non-redundant association rule mining requires generation of both closed itemsets and their minimal generators. However, only a few researchers have addressed both the issues for data streams. Further, association rule mining is now considered as multiobjective problem where multiple measures like correlation coefficient, recall, comprehensibility, lift etc can be used for evaluating a rule. Discovery of multiobjective association rules in data streams has not been paid much attention. In this paper, we have proposed a 3-step algorithm for generation of multiobjective non-redundant association rules in data streams. In the first step, an online procedure generates closed itemsets incrementally using state of the art CLICI algorithm and stores the results in a lattice based synopsis. An offline component invokes the proposed genMG and genMAR procedures whenever required. Without generating candidates, genMG computes minimal generators of all closed itemsets stored in the synopsis. Next, genMAR generates multiobjective association rules using non-dominating sorting based on user specified interestingness measures that are computed using the synopsis. Experimental evaluation using synthetic and real life datasets demonstrates the efficiency and scalability of the proposed algorithm.