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

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

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

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

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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 AlSi_(7)/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 AlSi_(7)/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.

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《International journal of computer aided engineering and technology》 |2022年第4期|412-430|共19页
作者
Pruthviraju Garikapati; K. Balamurugan; T.P. Latchoumi;
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作者单位

Department of Mechanical Engineering, VFSTR (Deemed to be University);

Department of Computer Science and Engineering, VFSTR (Deemed to be University);

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正文语种英语
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abrasive water jet machine; AWJM; partitioning algorithm; K-means; clustering; regression;

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

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