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Modified Structural and Attribute Clustering Algorithm for Improving Cluster Quality in Data Mining: A Quality Oriented Approach

机译:改进的结构和属性聚类算法,用于提高数据挖掘中的簇质量:一种面向质量的方法

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摘要

The need of Data mining is because of the explosive growth of data from terabytes to petabytes. Data mining preprocess aims to produce the quality mining result in descriptive and predictive analysis. The quality of a clustering result depends on both the similarity measure used by the method and its implementation. A straightforward way to combine structural and attribute similarities is to use a weighted distance function. Clustering results are arrived based on attribute similarities. The clusters balance the attribute and structural similarities. The existing Structural and Attribute cluster algorithm is analyzed and a new algorithm is proposed. Both the algorithms are compared and results are analyzed. It is found that the modified algorithm gives better quality clusters.
机译:数据挖掘的需求是由于数据从TB到PB的爆炸性增长。数据挖掘预处理旨在通过描述性和预测性分析产生高质量的挖掘结果。聚类结果的质量取决于该方法使用的相似性度量及其实现。组合结构和属性相似性的一种直接方法是使用加权距离函数。聚类结果是基于属性相似性得出的。集群平衡属性和结构相似性。分析了现有的结构和属性聚类算法,提出了一种新的算法。比较了两种算法并分析了结果。发现该改进算法给出了更好的聚类质量。

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