图 书 馆
挖掘频繁项的前K在一个灵活的滑动窗口的数据流Mining Top-K Frequent Items in a Data Stream with Flexible Sliding Windows |
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| 课程网址: | http://videolectures.net/kdd2010_thanh_lam_mtf/ |
| 主讲教师: | Hoang Thanh Lam |
| 开课单位: | 埃因霍温工业大学 |
| 开课时间: | 2010-10-01 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We study the problem of finding the k most frequent items in a stream of items for the recently proposed max-frequency measure. Based on the properties of an item, the max-frequency of an item is counted over a sliding window of which the length changes dynamically. Besides being parameterless, this way of measuring the support of items was shown to have the advantage of a faster detection of bursts in a stream, especially if the set of items is heterogeneous. The algorithm that was proposed for maintaining all frequent items, however, scales poorly when the number of items becomes large. Therefore, in this paper we propose, instead of reporting all frequent items, to only mine the top-$k$ most frequent ones. First we prove that in order to solve this problem exactly, we still need a prohibitive amount of memory (at least linear in the number of items). Yet, under some reasonable conditions, we show both theoretically and empirically that a memory-efficient algorithm exists. A prototype of this algorithm is implemented and we present its performance w.r.t. memory-efficiency on real-life data and in controlled experiments with synthetic data. |
| 关 键 词: | 测量项目; 突发检测; 内存算法 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-29：zyk |
| 阅读次数: | 34 |