A spatial filtering inspired three-way clustering approach with application to outlier detection

Three-way clustering provides an effective framework for clustering of data in the presence of uncertain, imprecise and incomplete data. In this article, we used ideas inspired from two commonly used spatial filters from image processing called minimum and maximum filters to construct a three-way clustering approach named RE3WC and explore its application in outlier detection. A three-way cluster is based on a pair of sets known as the core and support of a cluster. Given the results of a hard clustering algorithm in the form of hard clusters, RE3WC uses reduction and elevation operations to shrink and enlarge a hard cluster into the core and support of a three-way cluster. The two sets are then used to obtain the three regions of a three-way cluster namely, inside, outside and partial regions. Experimental results on CHAMELEON and other similar datasets indicate that the RE3WC can detect an additional 2.5% to 4.6% of objects as outliers that went undetected with clustering algorithms that detect outliers. The RE3WC results in more compact and precise clusters when applied on top of clustering algorithms that only provide partitioning of the data. Finally, RE3WC produces comparable results to some of the notable approaches such as LOF, LoOP, ABOD and IF. (C) 2020 Elsevier Inc. All rights reserved.

Automated summary

Three-way clustering is effective for handling uncertain, imprecise, and incomplete data, utilizing reduction and elevation operations to create core and support clusters. Experimental results show that RE3WC can detect additional outliers compared to other clustering algorithms, resulting in more compact and precise clusters. Additionally, RE3WC yields comparable results to notable approaches such as LOF, LoOP, ABOD, and IF.