Abstract:

Aiming to improve the clustering quality and efficiency on banking services datasets, this paper proposes the concepts of cluster diameter and the similarity measurement between clusters. It modifies multi-dimensional data to one dimension by dimension reduction based on distance order. It clusters the one dimension data with a self-Adaptive Sort Clustering(ASC) algorithm. This paper conducts extensive experiments to show that this algorithm can improve the cluster similarity and reduce the clustering time compared with Cobweb and K-means algorithms. The cluster similarity can be approximately improved by 200%.

Key words: cluster diameter, cluster similarity, self-Adaptive Sort Clustering(ASC) algorithm, dimension reduction by center distance order

摘要：

为提高金融业务数据集上的聚类质量和聚类效率，提出簇的直径、簇间的相似度这2个概念。利用距离尺度降维的中心距序降维法，将多维数据降至一维，在一维上利用自适应排序聚类算法ASC聚类。该算法和传统的Cobweb算法、K-means算法做对比，实验表明该方法能提高簇间相似度，最大提高200%。

关键词: 簇直径, 簇间相似度, ASC算法, 中心距序降维

CLC Number: 

• TP311