Paper Info
Title
Mining closed itemsets in data stream using formal concept analysis
Abstract
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.
Year
DOI
Venue
2010
10.1007/978-3-642-15105-7_23
DaWaK
Keywords
Field
DocType
data stream environment,frequent itemsets,recent closed itemsets,candidate generation,formal concept analysis,concept lattice,closed itemsets,frequent closed itemsets,online data stream,non-redundant association rule,association rule
Data mining,On demand,Data stream,Computer science,Association rule learning,Landmark,Formal concept analysis,Database
Conference
Volume
ISSN
ISBN
6263
0302-9743
3-642-15104-3
Citations 
PageRank 
References 
9
0.55
18
Authors
3
Name
Order
Citations
PageRank
Anamika Gupta1161.78
Vasudha Bhatnagar218117.69
Naveen Kumar39312.32