Abstract
In clustering analysis, data clusters are usually associated with feature subsets rather than the whole space. Therefore, soft subspace clustering devote to find the corresponding subspace for each cluster by assigning weight to the features. However, since categorical data is qualitative rather than quantitative data, the study of subspace clustering for categorical data is relatively rare and challenging. Therefore, this paper presents a new two-step subspace clustering algorithm for categorical data. Firstly, an initial clustering method is proposed to obtain a reliable initial cluster structure, based on which, the feature-to-cluster groups are constructed by utilizing intrinsic relationships between features. Subsequently, the local and global clustering are defined to learn the local cluster relations between data objects and achieve the final clustering results, respectively. Experimental results on benchmark datasets demonstrate the effectiveness of proposed method.
This work was supported by the National Natural Science Foundation of China under Grant 61806131 and the Natural Science Foundation of Guangdong Province under Grant 2018A030310510.
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Jia, H., Dong, M. (2023). Subspace Clustering with Feature Grouping for Categorical Data. In: Jin, Z., Jiang, Y., Buchmann, R.A., Bi, Y., Ghiran, AM., Ma, W. (eds) Knowledge Science, Engineering and Management. KSEM 2023. Lecture Notes in Computer Science(), vol 14117. Springer, Cham. https://doi.org/10.1007/978-3-031-40283-8_21
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DOI: https://doi.org/10.1007/978-3-031-40283-8_21
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