Title: K-means partitioning approach to predict the error observations in small datasets

Authors: Pruthviraju Garikapati; K. Balamurugan; T.P. Latchoumi

Addresses: Department of Mechanical Engineering, VFSTR (Deemed to be University), Guntur, 522213, AP, India ' Department of Mechanical Engineering, VFSTR (Deemed to be University), Guntur, 522213, AP, India ' Department of Computer Science and Engineering, VFSTR (Deemed to be University), Guntur, 522213, AP, India

Abstract: The partitioning algorithm was used to identify the uncertainty and the similarity in large sets of databases. K values are set based on the models. The effect of change in k values from the lowest to the highest level was analysed for a small set of databases that are acquired through machining AlSi₇/63% SiC hybrid composite. An attempt has been made to identify the correlation between the k value clustered class and with a developed linear regression model. Further, the analysis was done to identify the critical machining observations that have a high error rate while on machining AlSi₇/63% SiC hybrid composite using abrasive water jet at the varied parameters condition. Taguchi L27 orthogonal array observations are clustered into different groups with a k value of 2 to 8. The study was limited to k = 8 because at this level, clustered classes have very few observations that make unfit to predict the model.

Keywords: abrasive water jet machine; AWJM; partitioning algorithm; K-means; clustering; regression.

DOI: 10.1504/IJCAET.2022.126601

International Journal of Computer Aided Engineering and Technology, 2022 Vol.17 No.4, pp.412 - 430

Received: 18 Feb 2020
Accepted: 15 Jul 2020
Published online: 31 Oct 2022 *

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