Incremental updating algorithm association rules

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It causes overhead the algorithm needs to scan entire database every time and repeat the process.

Incremental updating of mined association rules is challenging.

IUA uses association rules to mining the database, aiming at finding the potential information or finding the reasons from massive data. In this article, we propose a temporal association rule and its discovering algorithm with exponential smoothing filter in a large transaction database.

Through experimental results, we confirmed that this is more precise and consumes a shorter running time than existing temporal association rules.

Results of experiments conducted on both synthetic and real data sets show that SODRNN algorithm is both effective and efficient.

According to the undulation degree of sequence, the instance including stronger class information is chosen to enter the learning process firstly.

Reasonable learning sequence helps to strengthen the knowledge reserve of the classifier.

This article proposes an incremental learning algorithm based on the K-Nearest Neighbor.

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This paper focuses on an important research topic in data mining (DM) which heavily replies on the association rules. Vaidya, An efficient approximate protocol for privacy-preserving association rule mining.

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