 Novel Approaches to Identify Clusters Using Independent Components Analysis with ApplicationSaima Afzal, Muhammad Mutahir Iqbal, Ayesha Afzal, Hassan S. Bakouch, Sadiah M. A. Aljeddani and Tahir Mehmood Mathematical Problems in Engineering, 2023, vol. 2023, 1-13 Abstract: As a statistical and computational technique, independent component analysis (ICA) is employed to separate the source variables into statistically independent components. ICA methods have received growing attention as effective data mining tools. In this paper, two novel ICA-based approaches are proposed to identify the clusters of variables. The identified clusters reduce the dimensionality of the data in a natural way. The first approach, namely “Estimated Mixing Coefficients,†is based on the sum of squares of mixing coefficients, and the second approach, namely “Ranked R¯2,†uses the ranking pattern of R¯2 of the original and reconstructed series at predefined threshold levels. The proposed techniques are applied to financial time series data to validate their effectiveness. The main focus of the study is on the clustering of multivariate time series datasets using two new proposed approaches based on independent component analysis. The internal and external structures of clusters are also explored using different metrics. Both proposed techniques are compared with some existing clustering techniques. The experimental evaluation results show that the performance of the proposed techniques is better than the existing techniques. Date: 2023 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:4830716 DOI: 10.1155/2023/4830716 Access Statistics for this article More articles in Mathematical Problems in Engineering from Hindawi |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.