Authors: Osama Aljarrah; Richard De Groof
Addresses: Department of Industrial Engineering, Faculty of Engineering, Hashemite University, Zarqa, Jordan ' Department of Computer and Information Science, College of Engineering, University of Massachusetts Dartmouth, MA, USA
Abstract: Financial data analysis is becoming more vital as the data collected from daily operations are exponentially increasing in the presence of data mining. Many financial analysts hope to extract useful knowledge from the database in the decision-making process to achieve a competitive priority. In business, companies' key performance indicators are usually characterised in a high dimensional space and companies can be categorised using them. Many literatures proposed different approaches to classify companies using different dataset. Although a plethora of multivariate analysis has been available, the computational requirements with highly complex datasets are challenging the current clustering algorithms. This paper proposes a new strategy by integrating dimensional compression techniques with clustering. The former projects the data in a few major variability dimensions; the latter further clusters the projection into groups. An application case study is provided for illustration and verification. This procedure shows a promising potential in wide variety of business applications.
Keywords: clustering; k-mean cluster analysis; principal component analysis; PCA; singular value decomposition; SVD; kernel principal component; repeated measures; financial datasets; Jordan.
International Journal of Decision Sciences, Risk and Management, 2017 Vol.7 No.4, pp.299 - 315
Received: 30 May 2017
Accepted: 02 Apr 2018
Published online: 30 Jul 2018 *