关键词: 数据挖掘, 标签数据, 约束, 半监督聚类

Abstract: In many data mining domains，there is a large supply of unlabeled data but limited labeled data，which can be expensive to generate.Consequently，semi-supervised clustering，which uses a small amount of labeled data to aid unlabeled clustering，has become a topic of significant recent interest.This paper presents a new algorithm，called semi-supervised clustering algorithm based on constrains learning，which obtains the similarity and dissimilarity criterions of data objects，adjusts them in the process of clustering，and uses them to constrain and supervise clustering.Demonstrated the clustering algorithm with Gaussian dataset，and the experimental results confirm that the clustering algorithm significantly improves the accuracy and speed of clustering when given a relatively small amount of supervision.

Key words: data mining, labeled data, constrains, semi-supervised clustering