An efficient clustering algorithm for market basket data based on small large ratios

Ching-Huang Yun, Kun-Ta Chuang, Ming-Syan Chen
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引用次数: 42

Abstract

In this paper we devise an efficient algorithm for clustering market-basket data items. In view of the nature of clustering market basket data, we devise in this paper a novel measurement, called the small-large (abbreviated as SL) ratio, and utilize this ratio to perform the clustering. With this SL ratio measurement, we develop an efficient clustering algorithm for data items to minimize the SL ratio in each group. The proposed algorithm not only incurs an execution time that is significantly smaller than that by prior work but also leads to the clustering results of very good quality.
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基于小比大比的市场篮数据聚类算法
本文设计了一种高效的聚类算法。鉴于市场篮子数据聚类的性质,本文设计了一种新的度量方法,称为小-大(简称SL)比率,并利用该比率进行聚类。通过这种SL比率测量,我们为数据项开发了一种有效的聚类算法,以最小化每组中的SL比率。该算法的执行时间明显小于之前的算法,而且聚类结果质量非常好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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