Online pattern mining for high-dimensional data streams
Online pattern mining for high-dimensional data streams
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DOI：
10.1109/bigdata.2015.7364109
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发表时间：
2015-10
期刊：
影响因子：
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通讯作者：
Yoshitaka Yamamoto;K. Iwanuma
中科院分区：
文献类型：
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作者：
Yoshitaka Yamamoto;K. Iwanuma
研究了流数据挖掘中的一次扫描近似算法。尽管模式发现在流数据中很重要，但这个问题在大数据社区中还没有得到充分解决。在这方面，我们简要回顾了以前提出的SDM方法。最近有一项工作是使用在线压缩技术来改善它们的局限性。它基于Δ-覆盖的概念。然后，我们介绍了它们，并显示从高维流交易，其中每一个由约1万个项目获得的实验结果。因此，结果表明我们可以显着提高SDM在维度数上的可扩展性。
This paper studies one-scan approximation algorithms for streaming data mining (SDM). Despite of the importance of pattern discovery in streaming data, this issue has not sufficiently addressed yet in the big data community. In this context, we briefly review the previously proposed SDM methods. There is a recent work to improve their limitation using the tecnique of online compression. It is based on the notion of Δ-cover. We then introduce them and show the experimental results obtained from high dimensional streaming transactions, each of which consists of about 10 thousand items. Consequently, the results demonstrate that we can drastically improve the scalability of SDM on the dimension number.