Keywords:-
Keywords:
data analysis; PCA; SPQR; SVD; FCM; Clustering Silhouette
Article Content:-
Abstract
The rise of machine learning yields remarkable outcomes across fields. Phrases like big data, AI, and cloud computing are becoming commonplace. Yet, data abundance doesn't assure success. Numerous works address data preprocessing for information discovery. This study assesses three techniques on unsupervised clustering, spotlighting SPQR's novel application. Findings stress preprocessing's data impact, urging caution against oversimplified datamining solutions.
References:-
References
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2. G.H. Golub and C.F. Van Loan, Matrix Computations, 3rd, Johns Hopkins, 1996, ISBN 978-0-8018-5414-9.
3. I.T. Jolliffe and J. Cadima, "Principal component analysis: a review and recent developments". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 374 (2065): 20150202
4. G.W. Stewart, “Four algorithms for the efficient computation of truncated pivoted QR approximations to a sparse matrix”. Numer. Math. 83, 313–323 (1999)
5. M. Popolizio, A. Amato, V. Piuri and V. Di Lecce, "Improving Classification Performance Using The Semi-Pivoted QR approximation algorithm", 7-8 January 2022 2nd FICR International Conference on Rising Threats in Expert Applications and Solutions
6. M. Koklu and I.A. Ozkan “Multiclass Classification of Dry Beans Using Computer Vision and Machine Learning Techniques”. Comput. Electron. Agric. 2020, 174, 105507.
7. J. Minguilln, J. Meneses, E. Aibar, N. Ferran-Ferrer and S. Fãbregues “Exploring the gender gap in the Spanish Wikipedia: Differences in engagement and editing practices”. PLoS ONE 2021, 16, e0246702.
8. P. J. Rousseeuw, “Silhouettes: A graphical aid to the interpretation and validation of cluster analysis”, Journal of Computational and Applied Mathematics, Volume 20, 1987, Pages 53-65, ISSN 0377-0427.
9. D. Dua and C. Graff (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science
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Popolizio, M., Amato, A., Dario, R., & Lecce, V. D. (2023). Data Dimensionality Reduction Algorithms for Machine Learning. International Journal Of Mathematics And Computer Research, 11(9), 3713-3715. https://doi.org/10.47191/ijmcr/v11i9.02