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
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
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
Frequent closed itemset based algorithms: a thorough structural and analytical survey
ACM SIGKDD Explorations Newsletter
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
A framework for incremental generation of closed itemsets
Discrete Applied Mathematics
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
Mining frequent closed itemsets from a landmark window over online data streams
Computers & Mathematics with Applications
GC-tree: a fast online algorithm for mining frequent closed itemsets
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Frequent pattern mining from time-fading streams of uncertain data
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
Lattice based associative classifier
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
Mining of multiobjective non-redundant association rules in data streams
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
Incrementally building frequent closed itemset lattice
Expert Systems with Applications: An International Journal
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Mining of frequent closed itemsets has been shown to be more efficient than mining frequent itemsets for generating non-redundant association rules. The task is challenging in data stream environment because of the unbounded nature and no-second-look characteristics. In this paper, we propose an algorithm, CLICI, for mining all recent closed itemsets in landmark window model of online data stream. The algorithm consists of an online component, which processes the transactions arriving in the stream without candidate generation and updates the synopsis appropriately. The offline component is invoked on demand to mine all frequent closed itemsets. User can explore and experiment by specifying the support threshold dynamically. The synopsis, CILattice, stores all recent closed itemsets in the stream. It is based on Concept Lattice - a core structure of Formal Concept Analysis (FCA). Closed itemsets stored in the form of lattice facilitate generation of non-redundant association rules and is the main motivation behind using lattice based synopsis. Experimental evaluation using synthetic and real life datasets demonstrates the scalablility of the algorithm.