A Survey on Frequent ItemSet Mining Over Data Stream

Rajesh Rawat; Prof. Nidhi Jain

The growing importance of data streams from a wide range of advanced applications such as fraud detection and learning trend has led to the study of Frequent Item-Set Mining over Data Stream. A data stream is an ordered sequence of instances that arrive at a rate that does not permit to permanently store data in memory. A frequent item-set is a set of items that appears at least in a pre-specified number of transactions. Frequent item-sets are typically used to generate association rules. In this paper we are discussing different type windowing techniques and the important algorithms available in this mining process.

Select Volume / Issues:

• Volume 4 Issue 1, Jan. 2013

Year:

2013

Type of Publication:

Article

Keywords:

Data Stream Mining; Frequent Itemset; Association Rule Mining; Windowing Techniques

Journal:

IJECCE

Volume:

4

Number:

1

Pages:

86-87

Month:

January