Mining Closed Itemsets in Data Stream Using Formal Concept Analysis

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 ...

• Type: Conference
• Year: 2010
• Where: DAWAK
• Authors: Anamika Gupta, Vasudha Bhatnagar, Naveen Kumar
• Keywords: Closed Itemsets, DAWAK 2010, Frequent Closed Itemsets, Information Technology, Non-redundant Association Rules