Research on a scalable parallel data mining algorithm
文献类型：会议论文
| 作者 | Wang ; Jinlin1 ; 2 ; Chen ; Xi1 ; Zhou ; Kefa1 |
| 出版日期 | 2009 |
| 会议名称 | 5th International Joint Conference on INC, IMS, and IDC |
| 会议日期 | 2009 |
| 会议地点 | Seoul, Korea, Republic of |
| 关键词 | Computational complexity - Computational efficiency - Information management - Parallel algorithms - Research - Semiconductor storage - Active field - Data mining algorithm - Domain of knowledge - Experimental verification - Hardware and software - Massive data - Network monitoring systems - Parallel data mining - Projection database - Real-world application - Sensor grids - Sequential patterns - Sequential-pattern mining - Spatial complexity - Streaming data |
| 页码 | 888-893 |
| 中文摘要 | 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. © 2009 IEEE. (16 refs.) |
| 收录类别 | EI |
| 会议录 | NCM 2009 - 5th International Joint Conference on INC, IMS, and IDC
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| ISBN号 | 13: 9780769537696 |
| 源URL | [http://ir.xjlas.org/handle/365004/10852]  |
| 专题 | 新疆生态与地理研究所_中国科学院新疆生态与地理研究所(2010年以前数据) |
| 推荐引用方式 GB/T 7714 | Wang,Jinlin1,2,et al. Research on a scalable parallel data mining algorithm[C]. 见:5th International Joint Conference on INC, IMS, and IDC. Seoul, Korea, Republic of. 2009. |
入库方式： OAI收割
来源：新疆生态与地理研究所
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