Data mining is a part of know ledge Discovery in database process (KDD). As technology advances, floods of data can be produced and shared in many appliances such as wireless Sensor networks or Web click streams. This calls for extracting useful information and knowledge from streams of data. In this paper, We have proposed an efficient algorithm, where, at any time the current frequencies of all frequent item sets can be immediately produced. The current frequency of an item set in a stream is defined as its maximal frequency over all possible windows in the stream from any point in the past until the current state. The experimental result shows the proposed algorithm not only maintains a small summery of information for one item set but also consumes less memory then existing algorithms for mining frequent item sets over recent data streams.
Panigrahi, Nibedita; Pattnaik, P.K.; and Padhi, S.K.
"An Efficient Algorithm for Mining Of frequent items using incremental model,"
International Journal of Computer Science and Informatics: Vol. 1:
1, Article 5.
Available at: https://www.interscience.in/ijcsi/vol1/iss1/5