Abstract
As one of the most import machine learning technique, clustering is widely used in many data classification areas. Due to the unsupervised learning feature, the quality of the clustering results needed to be evaluated. In this paper, the MPdist (midpoint-involved distance) based on the midpoint of centers between two clusters is firstly defined to measure the inter-cluster separation. Then, the MPC (MPdist based clustering validity index), a novel internal clustering validity index based on the combination of the new defined inner-cluster compactness and the inter-cluster separation, is proposed to effectively evaluate the validity of the clustering results of many clustering algorithms. Experimental results on testing many types of datasets have demonstrated that the MPC index proposed in this paper is able to quickly handle datasets like spherical datasets, non-spherical datasets and real large-scale datasets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Xie, J., Xiong, Z., Dai, Q., Wang, X., Zhang, Y.: A new internal index based on density core for clustering validation. Inf. Sci. 506, 346–365 (2020)
Adolfsson, A., Ackerman, M., Brownstein, N.C.: to cluster, or not to cluster: an analysis of clusterability methods. Pattern Recogn. 88, 13–26 (2019)
Rathore, P., Bezdek, J.C., Erfani, S.M., Rajasegarar, S., Palaniswami, M.: Ensemble fuzzy clustering using cumulative aggregation on random projections. IEEE Trans. Fuzzy Syst. 26(3), 1510–1524 (2018)
Zhu, E., Ma, R.: An effective partitional clustering algorithm based on new clustering validity index. Appl. Soft Comput. 71, 608–621 (2018)
Calinski, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat. 3(1), 1–27 (1974)
Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 22, 53–65 (1987)
Gurrutxaga, I., et al.: SEP/COP: an efficient method to find the best partition in hierarchical clustering based on a new cluster validity index. Pattern Recogn. 43, 3364–3373 (2010)
Yue, S., Wang, J., Wang, J., Bao, X.: A new validity index for evaluating the clustering results by partitional clustering algorithm. Soft. Comput. 20(3), 1127–1138 (2016)
Starczewski, A.: A new validity index for crisp clusters. Pattern Anal. Appl. 20, 687–700 (2017)
Zhu, E., Zhang, Y., Wen, P., Liu, F.: Fast and stable clustering analysis based on grid-mapping K-means algorithm and new clustering validity index. Neurocomputing 363, 149–170 (2019)
Zhou, S., Zhenyuan, X., Liu, F.: Method for determining the optimal number of clusters based on agglomerative hierarchical clustering. IEEE Trans. Neural Netw. Learn. Syst. 28(12), 3007–3017 (2017)
Laurens van der Maaten. t-SNE. Available at: https://lvdmaaten.github.io/tsne
Author, F., Author, S.: Title of a proceedings paper. In: Editor, F., Editor, S. (eds.) Conference 2016, LNCS, vol. 9999, pp. 1–13. Springer, Heidelberg (2016). https://doi.org/10.10007/1234567890
Acknowledgments
This study was supported by the Natural Science Foundation of Anhui Province (China)(No. 2008085MF188) and the University Natural Science Research Project of Anhui Province (China) (No. KJ2021A0041).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zuo, Y., Ma, Z., Zhu, E. (2024). MPC: A Novel Internal Clustering Validity Index Based on Midpoint-Involved Distance. In: Tari, Z., Li, K., Wu, H. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2023. Lecture Notes in Computer Science, vol 14489. Springer, Singapore. https://doi.org/10.1007/978-981-97-0798-0_18
Download citation
DOI: https://doi.org/10.1007/978-981-97-0798-0_18
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-0797-3
Online ISBN: 978-981-97-0798-0
eBook Packages: Computer ScienceComputer Science (R0)