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An improved incremental algorithm for mining weighted class-association rules

挖掘加权类关联规则的一种改进增量算法

1Department of Computer Science and Engineering, Thiagarajar College of Engineering, Madurai, India

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2Department of Computer Science and Engineering, Thiagarajar College of Engineering, Madurai, India

Abstract

Constructing fast and accurate classifiers for large data sets is an important task in data mining. Associative classification can produce more efficient and accurate classifiers than traditional classification techniques. Weighted class association rule (WCAR) mining reflects significance of items by considering their weight. Moreover, real time databases are dynamic. This influences the need for incremental approach for classification. Existing incremental classification algorithms suffer from issues like longer execution time and higher memory usage. This paper proposes an algorithm which uses hash structure to store weighted frequent items and the concept of difference of object identifiers to compute the support faster. For mining incremental databases, pre-large concept is used to reduce the number of re-scans over the original database. The proposed algorithm was implemented and tested on experimental data sets taken from UCI repository. The results show that proposed algorithm for mining WCARs gives better results compared to existing algorithm.

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摘要

为大数据集构建快速准确的分类器是数据挖掘中的一项重要任务。与传统的分类技术相比，关联分类可以产生更有效和准确的分类器。加权类关联规则（WCAR）挖掘通过考虑其权重来反映项的重要性。而且，实时数据库是动态的。这会影响对增量分类方法的需求。现有的增量分类算法面临诸如更长的执行时间和更高的内存使用率等问题。本文提出了一种算法，该算法使用哈希结构存储加权频繁项和对象标识符差异的概念，以更快地计算支持。对于挖掘增量数据库，使用pre-large概念来减少原始数据库上的re-scans次数。所提出的算法在从UCI存储库获取的实验数据集上实现和测试。结果表明，与现有算法相比，所提出的WCAR挖掘算法效果更好。

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