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Clustering mixed type data: a space structure-based approach

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Abstract

Clustering mixed type data is important for the areas such as knowledge discovery and machine learning. Although many clustering algorithms have been developed for mixed type data, clustering mixed type data is still a challenging task. The challenges mainly come from the fact that the numerical attributes and categorical attributes of mixed type data are not in the same space. Most of the mixed data clustering methods handle the two types of attributes separately. The gap between the numerical attributes and categorical attributes is not handled very well. To handle the above issues, we expand the space structure representation scheme for categorical data to mixed type data. In the new scheme, all the attributes of the mixed type data are expressed as the numerical type, which is in a Euclidean space. In addition, we propose an accelerated approximate space structure based on the Nyström method, which reduces the time cost and memory cost of constructing a space structure. We then propose general frameworks based on the space structure data (SBM) and accelerated approximate space structure (Ap-SBM) for mixed type data clustering. Experimental analyses reflect the ability of the space structure to express the original mixed type data and the ability of the accelerated approximate space structure to express the space structure. The experimental results on thirteen mixed type data sets from UCI show superiority of the proposed frameworks compared with the other six representative mixed type data clustering algorithms.

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Acknowledgements

This work was supported by National Key Research and Development Program of China (No. 2021ZD0112400), National Natural Science Foundation of China (Nos. 62136005, 62106132), the Shanxi Province Science Foundation for Youths (No. 201901D211168, 20210302124271, 202103021223026).

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  1. Feijiang Li and Yuhua Qian contributed equally to this work.

Authors and Affiliations

  1. Institute of Big Data Science and Industry, Shanxi University, Taiyuan, 030006, Shanxi, China

    Feijiang Li, Yuhua Qian, Jieting Wang & Furong Peng

  2. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Taiyuan, 030006, Shanxi, China

    Yuhua Qian & Jiye Liang

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  1. Feijiang Li
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  2. Yuhua Qian
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Correspondence to Yuhua Qian.

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Li, F., Qian, Y., Wang, J. et al. Clustering mixed type data: a space structure-based approach. Int. J. Mach. Learn. & Cyber. 13, 2799–2812 (2022). https://doi.org/10.1007/s13042-022-01602-x

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