Representative Points and Cluster Attributes Based Incremental Sequence Clustering Algorithm

Year, pages: 2017, 1361 - 1384

In order to improve the execution time and clustering quality of sequence clustering algorithm in large-scale dynamic dataset, a novel algorithm RPCAISC (Representative Points and Cluster Attributes Based Incremental Sequence Clustering) was presented. In this paper, density factor is defined. The primary representative point that has a density factor less than the prescribed threshold will be deleted directly. New representative points can be reselected from non-representative points. Moreover, the representative points of each cluster are modeled using the K-nearest neighbor method. The definition of the relevant degree (RD) between clusters was also proposed. The RD is computed by comprehensively considering the correlations of objects within a cluster and between different clusters. Then, whether the two clusters need to merge is determined. Additionally, the cluster attributes of the initial clustering are retained with this process. By calculating the matching degree between the incremental sequence and the existing cluster attributes, dynamic sequence clustering can be achieved. The theoretic experimental results and analysis prove that RPCAISC has better correct rate of clustering results and execution efficiency.

Wu, D., Ren, J. 2017. Representative Points and Cluster Attributes Based Incremental Sequence Clustering Algorithm. In Computing and Informatics, vol. 36, no.6, pp. 1361-1384. 1335-9150. DOI: https://doi.org/10.4149/cai_2017_6_1361

Wu, D., Ren, J. (2017). Representative Points and Cluster Attributes Based Incremental Sequence Clustering Algorithm. Computing and Informatics, 36(6), 1361-1384. 1335-9150. DOI: https://doi.org/10.4149/cai_2017_6_1361