A Neural-networks Associative Classification
Method For Association Rule Mining
Author(s)
P. Sermswatsri & C. Srisa-an
Abstract
Association rule theory has been used to analyze relationships between data in a
large database. It has been applied in various fields including market analysis,
customer habit study, detection, classification, clustering and so on. However, it
consumes a great amount of time. In the Apriori approach, all frequent patterns
are found and then generate association rules from above frequent patterns.
However, this approach generates a huge time-intensive query called “iceberg
query”. Various researches have been done under the Apriori-like approach to
improve performance of the frequent pattern mining task but the results were not
as much as expected due to many scans on the dataset. This research aims to
propose a flexible way of mining frequent patterns by extending the idea of the
Associative Classification method. For better performance, the Neural-Network
Associative Classification (NAC) method is proposed here to be one of the
approaches for building accurate and efficient classifiers. In this paper, the
Neural Networks Associative Classification method is used in order to improve
its accuracy. The structure of the network reflects the knowledge uncovered in
the previous discovery phase. The trained network is then used to classify unseen
data. The accuracy rates obtained from the datasets show promising results.
Keywords: associative classification, association rule, data mining, back
propagation neural networks.
1 Introduction
The current emphasis in Data Mining and Machine Learning research involves
improving the classifier to be more precise and efficient. The important Machine
Learning algorithms are Decision Trees [1], Rule Learning [2], Naïve-Bayes
Classifier [3], and Statistics [4]. They use Heuristic Search and Greedy Search
Keywords
associative classification, association rule, data mining, backpropagation neural networks.
Related Book
Other papers in this volume
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