A Single-Pass Online Data Mining Algorithm Combined with Control Theory with Limited Memory in Dynamic Data Streams

摘要

This paper addresses a fundamental problem that arises in data streaming scenarios, namely, today's data mining is ill-equipped to handle data streams effectively, and pays little attention to the network stability and the fast response. To the question, we present a control-theoretic explicit rate (ER) online data mining control algorithm (ODMCA) to regulate the sending rate of mined data, which accounts for the main memory occupancies of terminal nodes. The proposed method uses a distributed proportional integrative plus derivative controller combined with data-mining, where the control parameters can be designed to ensure the stability of the control loop in terms of sending rate of mined data. We further analyze the theoretical aspects of the proposed algorithm, and simulation results show the efficiency of our scheme in terms of high main memory occupancy, fast response of the main memory occupancy as well as of the controlled sending rates.