Two-Staged Clustering Algorithm for Two-Attributes-Set Problem

• Main Authors: Ya-Chun Hsiao, 蕭雅君
• Other Authors: Yen-Liang Chen
• Format: Others
• Language: en_US
• Online Access: http://ndltd.ncl.edu.tw/handle/88877684345020779629

碩士 === 國立中央大學 === 資訊管理研究所 === 98 === Cluster analysis has recently become a highly active topic in data mining research. However, existing clustering algorithms had a common problem for applying on practical application that they consider only one set of attributes for both partitioning data space and measuring similarity between objects when clustering data. There are some practical situations that two different sets of attributes are required for both procedures. For example, a bank needs to cluster their customers to learn about customers’ consumption behaviors of different background. Then customers should be clustered by the attribute set of consumption behaviors, while the bank still need to know the characteristics of every cluster from the customers’ personal information like age and income. Therefore, two different sets of attributes are required that one set is for similarity-measuring, called similarity-measuring attribute, and the other one, called dataset-partitioning attribute, is for partitioning data set as well as describing resulting clusters. Traditional algorithms do not distinguish the two sets of attributes which lead to low quality clustering results in such two-attributes-set problem. We propose Two-Clustering Algorithm to solve the two-attributes-set problem, generating resulting clusters that can be segmented or described by dataset-partitioning attributes and objects in the same cluster are similar in similarity-measuring attributes as well.