Research on a Scalable Parallel Data Mining Algorithm

• Published in: 2009 Fifth International Joint Conference on INC, IMS and IDC pp. 888 - 893
• Main Authors: Jinlin Wang, Xi Chen, Kefa Zhou
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2009
• Subjects: Algorithm design and analysis; Application software; Concurrent computing; Data mining; Hardware; Mesh generation; Monitoring; parallel algorithm; Parallel algorithms; Sensor systems and applications; sequential patterns; Spatial databases
• ISBN: 1424452090; 9781424452095
• DOI: 10.1109/NCM.2009.330

Sequential pattern mining is an active field in the domain of knowledge discovery and has been widely studied for over a decade by data mining researchers. More and more, with the constant progress in hardware and software technologies, real-world applications like network monitoring systems or sensor grids generate huge amount of streaming data. These works need an efficient and scalable parallel algorithm. On the basis of the widespread problem in current sequential pattern data mining algorithm and researching the data mining algorithm of serial sequential pattern, this paper proposes sequential patterns based and projection database based algorithm for scalable parallel sequential patterns data mining algorithm. Through theoretical analysis and experimental verification, the parallel data mining algorithm can well reduce the computational and spatial complexity and improve the efficiency of data mining in massive data circumstances.