Online pattern mining for high-dimensional data streams

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.