Abstract
As an unsupervised machine learning method, clustering is an important approach to understanding structural information in data. However, current adaptive clustering approach using multi-objective optimization framework have two apparent limitations. The first is that prior knowledge is needed to identify the correct cluster number. The second is difficulty in evaluating the best clustering solutions from the Pareto Optimal Front (POF) generated by a multi-objective optimization. These problems become severer in non-category datasets. Therefore, the primary goal of this research is to establish a genetic optimization based multi-objective clustering framework, in which multiple clustering validity indexes (CVIs) can be tested simultaneously to automatically obtain the optimal cluster number without knowing any sample label information in advance. In this effort, we will not only be able to consider clustering measurements such as cluster cohesion and separation, but also take other aspects, such as compactness, connectivity, variation among data elements, into consideration as well. Then, we aim to design a procedure to recommend three best solutions from the POF by using appropriate combination of CVIs without increasing computational cost. This procedure is expected to control the cluster number in a reasonable range and consequently decrease the difficulty in best solution recommendation. Finally, since we have the knowledge that using gene rearrangement in the genetic optimization does not affect partition, we take this advantage to merge clusters effectively and significantly speed the convergence of the algorithm. Our approach can outperform the state-of-the-art counterparts across diverse benchmark datasets in terms of partitioning accuracy and performance, as demonstrated in three experiments conducted on both artificial and typical real-world datasets.
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This research was funded by the National Natural Science Foundation of China (61871061), which is gratefully acknowledged.
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Qu, H., Yin, L., Tang, X. (2022). Multi-objective Automatic Clustering with Gene Rearrangement and Cluster Merging. In: Sgurev, V., Jotsov, V., Kacprzyk, J. (eds) Advances in Intelligent Systems Research and Innovation. Studies in Systems, Decision and Control, vol 379. Springer, Cham. https://doi.org/10.1007/978-3-030-78124-8_5
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