SEMI-SUPERVISED K-MEANS CLUSTERING FOR MULTI-TYPE RELATIONAL DATA

摘要：

In many data mining tasks, there is a large supply of unlabeled data but limited labeled data since it is expensive generated. Therefore, a number of semi-supervised clustering algorithms have been proposed, but few of them are specially designed for multi-type relational data. In this paper, a semi-supervised A-means clustering algorithm for multi-type relational data is proposed, which is based on the combination of semi-supervised /(-means method and multi-type relational data clustering. In order to achieve high performance, in the algorithm, we first analyze all kinds of relationships in data, which include intra-relationship, inter-relationship, explicit and implicit relationship; and then extend /-means clustering algorithm by seeding and new similarity measures, where attributes information, labeled data and all kinds of relationships are employed. The experimental results show the effectiveness of our method.

关键词： Semi-supervised learning clustering algorithm multi- type relational data

作者: YING GAO HONG QI DA-YOU LIU HE LIU

作者单位: College of Computer Science and Technology, Jilin University, Changchun, 130012, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 326-330

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）