Mining frequent itemsets in data stream applications is beneficial for a number of purposes such as knowledge discovery, trend learning, fraud detection, transaction prediction and estimation. In data streams, new data are continuously coming as time advances. It is costly even impossible to store all streaming data received so far due to the memory constraint. It is assumed that the stream can only be scanned once and hence if an item is passed, it can not be revisited, unless it is stored in main memory. Storing large parts of the stream, however, is not possible because the amount of data passing by is typically huge. In this paper, we study the problem of finding frequent items in a continuous stream of items. A new frequency measure is introduced, based on a variable window length. We study the properties of the new method, and propose an incremental algorithm that allows producing the frequent itemsets immediately at any time. In our method, we used multiple s egments for handling different size of windows. By storing these segments in a data structure, the usage of memory can be optimized. Our experiments show that our algorithm performs much better in optimizing memory usage and mining only the most recent patterns in very less time.
Comments: 9 Pages.
[v1] 2012-08-18 21:45:40
Unique-IP document downloads: 93 times
Add your own feedback and questions here:
You are equally welcome to be positive or negative about any paper but please be polite. If you are being critical you must mention at least one specific error, otherwise your comment will be deleted as unhelpful.