Abstract
Segmentation also called clustering is the most important means of data mining. It is an unsupervised learning technique that may be used to split a large dataset into groups. In this work, we propose a new clustering technique that combines the well-known k-means clustering technique with a stochastic local search meta-heuristic. The proposed method is applied to cluster creditworthy customers/companies against non-credit worthy ones in credit scoring. Empirical studies are conducted on five financial datasets. The numerical results are interesting and show the benefits of the proposed technique for banks and clients segmentation.
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Web site of the considered datasets. https://archive.ics.uci.edu/ml/datasets
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Boughaci, D., Alkhawaldeh, A.A.K. (2019). Stochastic Local Search Based Feature Selection Combined with K-means for Clients’ Segmentation in Credit Scoring. In: Alfaries, A., Mengash, H., Yasar, A., Shakshuki, E. (eds) Advances in Data Science, Cyber Security and IT Applications. ICC 2019. Communications in Computer and Information Science, vol 1097. Springer, Cham. https://doi.org/10.1007/978-3-030-36365-9_10
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