Start » Publications

Mining Frequent Itemsets in Time-Varying Data Streams

Yingying Tao, M. Tamer Özsu

Conference Proceeding: 01/2009; In proceeding of: Proc. 18th ACM Int. Conf. on Information and Knowledge Management

Abstract

Mining frequent itemsets in data streams is beneficial to many real-world applications but is also a challenging task since data streams are unbounded and have high arrival rates. Moreover, the distribution of data streams can change over time, which makes the task of maintaining frequent itemsets even harder. In this paper, we propose a falsenegative oriented algorithm, called TWIM, that can find most of the frequent itemsets, detect distribution changes, and update the mining results accordingly. Experimental results show that our algorithm performs as good as other false-negative algorithms on data streams without distribution change, and has the ability to detect changes over time-varying data streams in real-time with a high accuracy rate.

Comments on this publication

ResearchGate members can add comments. Sign up now and post your comment!

Similar publications

Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.

Science & Research Jobs

View jobs