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Received:October 11, 2010 Revised:November 30, 2010
Received:October 11, 2010 Revised:November 30, 2010
中文摘要: 为了更好的挖掘数据流,对传统的滑动窗口机制进行改进,提出一种大小可变的滑动窗口机制的数据流频繁集挖掘算法DS-stream 算法。该算法能够根据数据流的数据分布变化自适应调整窗口大小,节省了没必要的空间与时间消耗。算法采用一种分区窗口机制,结合基本窗口和时间窗口,同时考虑数据流的海量特性和时变特性,利用前缀树的概要数据结构。实验结果表明, DS-stream 算法在挖掘数据流频繁集上有很好的时间与空间效率。
Abstract:To mine data stream efficiency, a new slide window, combines basic window and time window, adopts a
scheme of window based on sector. It could apply to the real data streams and change the size of the windows, and save
time and space that no necessary to use. And the algorithm based on frequent itemsets mining-DS-stream could
self-adaptively adjust the size of time windows, considering the large number and chronotropic character of the data
stream. In the algorithm, transaction list group is adopted as synopsis data structure. The experiments results in Eclipse
indicate that the DS-stream algorithm is more effective in term of temporal and spatial performance.
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苏勇,范玉玲.可变滑动窗口在数据流频繁模式挖掘上的应用.计算机系统应用,2011,20(6):200-202
SU Yong,FAN Yu-Ling.Data stream of Closed Pattern Mining Based on Variable Slide Window.COMPUTER SYSTEMS APPLICATIONS,2011,20(6):200-202
苏勇,范玉玲.可变滑动窗口在数据流频繁模式挖掘上的应用.计算机系统应用,2011,20(6):200-202
SU Yong,FAN Yu-Ling.Data stream of Closed Pattern Mining Based on Variable Slide Window.COMPUTER SYSTEMS APPLICATIONS,2011,20(6):200-202