Sliding Window-based Frequent Itemsets Mining over Data Streams using Tail Pointer Table

DOI

10.1080/18756891.2013.859860How to use a DOI?

Keywords

data mining, data streams, frequent itemsets, sliding window, tail pointer table

Abstract

Mining frequent itemsets over transaction data streams is critical for many applications, such as wireless sensor networks, analysis of retail market data, and stock market predication. The sliding window method is an important way of mining frequent itemsets over data streams. The speed of the sliding window is affected not only by the efficiency of the mining algorithm, but also by the efficiency of updating data. In this paper, we propose a new data structure with a Tail Pointer Table and a corresponding mining algorithm; we also propose a algorithm COFI2, a revised version of the frequent itemsets mining algorithm COFI (Co-Occurrence Frequent-Item), to reduce the temporal and memory requirements. Further, theoretical analysis and experiments are carried out to prove their effectiveness.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Le Wang
AU  - Lin Feng
AU  - Bo Jin
PY  - 2014
DA  - 2014/02/03
TI  - Sliding Window-based Frequent Itemsets Mining over Data Streams using Tail Pointer Table
JO  - International Journal of Computational Intelligence Systems
SP  - 25
EP  - 36
VL  - 7
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2013.859860
DO  - 10.1080/18756891.2013.859860
ID  - Wang2014
ER  -