Clustering Transactional Data
Clustering Transactional Data
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DOI：
10.1007/3-540-45681-3_15
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发表时间：
2002-08
期刊：
影响因子：
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通讯作者：
F. Giannotti;C. Gozzi;G. Manco
中科院分区：
文献类型：
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作者：
F. Giannotti;C. Gozzi;G. Manco
本文提出了一种能够管理事务的划分方法，即可变大小的分类数据的元组。我们采用K-均值算法中数学距离的标准定义来表示事务之间的差异，并雷德细化聚类中心的概念。聚类中心被用作聚类元素的共同属性的代表。我们表明，使用我们的集群质心的概念与Jaccard距离，我们得到的结果是qualitywith最常用的事务聚类方法，但substantiallyi mprove他们的效率。
In this paper we present a partitioning method capable to manage transactions, namelyt uples of variable size of categorical data. We adapt the standard definition of mathematical distance used in theK- Means algorithm to represent dissimilarityam ong transactions, and rede fine the notion of cluster centroid. The cluster centroid is used as the representative of the common properties of cluster elements. We show that using our concept of cluster centroid together with Jaccard distance we obtain results that are comparable in qualityw ith the most used transactional clustering approaches, but substantiallyi mprove their efficiency.