Many current sequence clustering algorithms are based on the hypothesis that sequence can be characterized by its local features, without differentiating between global similarity and local similarity of sequences in different applications, which is applicable to biological sequences such as DNA and protein with conserved sub-patterns. However, in some domains such as the comparison of customers’ purchase behaviors in retail transaction database and the pattern match in time series data, due to difficulties in forming frequent sub-pattern, it is more reasonable to cluster these sequence data based on global similarity. Besides, among sequence clustering algorithms based on local similarity, the ability that sub-patterns characterize sequence should be improved. So, this paper proposes two clustering algorithms, GSClu (global similarity clustering) and LSClu (local similarity clustering), for different application fields, based on global and local similarity respectively. GSClu uses bisecting k-means technique and CSClu adopts sub-patterns with gap constraint to cluster the sequence data of corresponding application field. Sequence data in the experiments include retail transaction data and protein data. The experimental results show that GSClu and LSClu are of fast processing rate and high clustering quality.