ICE: Incremental Subspace Clustering of High-Dimensional Categorical Data
Ning Pang, Chaowei Zhang, Jifu Zhang, Xiao Qin · International Journal of Uncertainty Fuzziness and Knowledge-Based Systems · 2025
Subspace clustering is an effective way to analyze high-dimensional data. The main problems of the conventional subspace clustering techniques are as follows: first, conventional clustering methods can not describe categorical attribute space in more detail; second, most subspace-clustering techniques failure to process dynamic data effectively; finally, lack of effective noise recognition leads to the decline of the efficiency of incremental subspace-clustering analysis. We address the above problems by an incremental subspace-clustering algorithm — called ICE. With attribute subspace constructed by a rough set-based weight computing method, ICE obtains clustering results through initial and incremental clustering stage. Utilizing the original cluster results generated from initial clustering stage, we adopt merging and splitting operation to dynamic adjust cluster-structure in incremental clustering stage. Before achieving the final results, a polymerization-based noise recognition technique is employed to automatically identify noise from sparse clusters without human threshold intervention. We implement ICE on synthetic and real-world datasets. The experimental results reveal that incremental subspace-clustering method can achieves satisfactory performance on extensibility, accuracy and robustness.