Discovering Frequent Patterns by Constructing Frequent Pattern Network over Data Streams in E-Marketplaces
The extracting useful information such as itemsets and frequent patterns from the data becomes very important in terms of marketing strategies and maximizing profit in e-marketplaces. Although existing algorithms mining frequent patterns from the data are useful for persistent databases, they have some limitations of data mining from dynamic data arising from the continuous, unbounded and high speed characteristics of data streams. To identify useful frequent patterns in data streams, this paper proposes a frequent pattern network and a new method for discovering frequent patterns through the approximation of frequency counting on the network. The frequent pattern network, whose vertices and edges represent summarized information of transaction data, provides a user-centered environment based on the process of continuously mining frequent patterns because the proposed network is a small and compact data structure, and flexible for minimum support value. The experimental results show that proposed method is more efficient than FP-growth and Apriori methods, and the discussion of memory usage demonstrates the efficiency of the proposed method.
frequent pattern network data stream approximation data structure frequent pattern mining