Abstract
The three-way clustering is different from the traditional two-way clustering. Instead of using two regions to represent a cluster by a single set, a cluster is represented by a pair of sets, and there are three regions such as the core region, fringe region and trivial region. The three-way representation intuitively shows that which objects are fringe to the cluster and it is proposed for dealing with uncertain clustering. However, the three-way clustering algorithm usually needs an appropriate evaluation function and corresponding thresholds. It is not scientific and efficient method for setting the thresholds in advance. Meanwhile, there is a large amount of mixed-type data in real life. Therefore, this paper proposes an adaptive three-way clustering algorithm for mixed-type data, which adjusts the three-way thresholds during the clustering process based on the idea of universal gravitation by excavating more detailed ascription relation between objects and clusters. The experimental results show that the proposed algorithm has good performance in indices such as the accuracy, F-measure, RI and NMI.
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
Similar content being viewed by others
References
Du, M., Ding, S., Jia, H.: Study on density peaks clustering based on k-nearest neighbors and principal component analysis. Knowl. Based Syst. 99, 135–145 (2016)
Lingras, P., West, C.: Interval set clustering of web users with rough k-means. J. Intell. Inf. Syst. 23(1), 5–16 (2004)
Lingras, P., Peters, G.: Rough clustering. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 1(1), 64–72 (2011)
Rand, W.M.: Objective criteria for the evaluation of clustering methods. Publ. Am. Stat. Assoc. 66(336), 846–850 (1971)
Rodriguez, A., Laio, A.: Machine learning. Clustering by fast search and find of density peaks. Science 344(6191), 1492–1496 (2014)
UCI Machine Learning Repository. http://archive.ics.uci.edu/ml
Wang, Y., Chen, L., Mei, J.P.: Incremental fuzzy clustering with multiple medoids for large data. IEEE Trans. Fuzzy Syst. 22(6), 1557–1568 (2014)
Wang, P.X., Yao, Y.Y.: CE3: a three-way clustering method based on mathematical morphology. Knowl. Based Syst. 155, 54–65 (2018)
Yao, Y.Y., Deng, X.F.: Three-way decisions with probabilistic rough sets. Inf. Sci. 180(3), 341–353 (2010)
Yu, H., Wang, X.C., Wang, G.Y., Zeng, X.H.: An active three-way clustering method via low-rank matrices for multi-view data. Inf. Sci. 000, 1–17 (2018)
Yu, H.: A framework of three-way cluster analysis. In: Polkowski, L., et al. (eds.) IJCRS 2017. LNCS (LNAI), vol. 10314, pp. 300–312. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60840-2_22
Yu, H., Chang, Z.H., Zhou, B.: A novel three-way clustering algorithm for mixed-type data. In: IEEE International Conference on Big Knowledge, pp. 119–126. IEEE (2017)
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61751312, 61533020 and 61379114.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Xiong, J., Yu, H. (2018). An Adaptive Three-Way Clustering Algorithm for Mixed-Type Data. In: Ceci, M., Japkowicz, N., Liu, J., Papadopoulos, G., Raś, Z. (eds) Foundations of Intelligent Systems. ISMIS 2018. Lecture Notes in Computer Science(), vol 11177. Springer, Cham. https://doi.org/10.1007/978-3-030-01851-1_36
Download citation
DOI: https://doi.org/10.1007/978-3-030-01851-1_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01850-4
Online ISBN: 978-3-030-01851-1
eBook Packages: Computer ScienceComputer Science (R0)