TDMCS: An Efficient Method for Mining Closed Frequent Patterns over Data Streams Based on Time Decay Model

In some data stream applications, the information embedded in the data arriving in the new recent time period is important than historical transactions. Because data stream is changing over time, concept drift problem may appear in data stream mining. Frequent pattern mining methods always generate useless and redundant patterns. In order to obtain the result set of lossless compression, closed pattern is needed. A novel method for efficiently mining closed frequent patterns on data stream is proposed in this paper. The main works includes: distinguished importance of recent transactions from historical transactions based on time decay model and sliding window model; designed the frame minimum support count-maximal support error rate-decay factor (θ-ε-f) to avoid concept drift; used closure operator to improve the efficiency of algorithm; design a novel way to set decay factor: average-decay-factor faverage in order to balance the high recall and high precision of algorithm. The performance of proposed method is evaluated via experiments, and the results show that the proposed method is efficient and steady-state. It applies to mine data streams with high density and long patterns. It is suitable for different size sliding windows, and it is also superior to other analogous algorithms.