Link-Based Cluster Ensemble Method for Improved Meta-clustering Algorithm
- 48 Downloads
Ensemble clustering has become a hot research field in intelligent information processing and machine learning. Although significant progress has been made in recent years, there are still two challenging issues in the current ensemble clustering research. First of all, most ensemble clustering algorithms tend to explore similarity at the level of object but lack the ability to explore information at the level of cluster. Secondly, many ensemble clustering algorithms only focus on the direct relationship, while ignoring the indirect relationship between clusters. In order to solve these two problems, a link-based meta-clustering algorithm (L-MCLA) have been proposed in this paper. A series of experiment results demonstrate that the proposed algorithm not only produces better clustering effect but is also less influenced by different ensemble sizes.
KeywordsInter-cluster similarity Ensemble clustering Clustering Connected triple Meta-clustering algorithm (MCLA)
This work is supported by the National Natural Science Foundation of China under Grant No. 61672522 and No. 61976216.
- 5.Løkse, S., Bianchi, F.M., Salberg, A.-B., Jenssen, R.: Spectral clustering using PCKID – a probabilistic cluster kernel for incomplete data. In: Sharma, P., Bianchi, F.M. (eds.) SCIA 2017. LNCS, vol. 10269, pp. 431–442. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59126-1_36CrossRefGoogle Scholar
- 6.Liu, R., Wang, H., Yu, X.: Shared-nearest-neighbor-based clustering by fast search and find of density peaks. Inf. Sci. (2018). https://doi.org/10.1016/j.ins.2018.03.031
- 12.Iam-On, N., Boongoen, T., Garrett, S.M., et al.: A link-based approach to the cluster ensemble problem. IEEE Trans. Softw. Eng. 33(12), 2396–2409 (2011)Google Scholar
- 14.Fern, X.Z., Brodley, C.E.: Solving cluster ensemble problems by bipartite graph partitioning. In: Proceedings of the Twenty-First International Conference on Machine Learning, p. 36. ACM (2004). https://doi.org/10.1145/1015330.1015414
- 18.Nguyen, B., De Baets, B.: Kernel-based distance metric learning for supervised k-means clustering. IEEE Trans. Neural Netw. Learn. Syst. 1–12 (2019). https://doi.org/10.1109/tnnls.2018.2890021
- 22.Asuncion, A., Newman, D.J.: UCI Machine Learning Repository (2007). http://www.ics.uci.edu/mlearn/MLRepository.html