Global Mean Based Nearest Feature Object Value Selection with Feature Creation Method for Clustering Accuracy Improvement

M PRASAD; t Strikanth

doi:10.20944/preprints202408.1036.v1

Preprint Article Version 1 This version is not peer-reviewed

Version 1 : Received: 13 August 2024 / Approved: 14 August 2024 / Online: 14 August 2024 (12:12:06 CEST)

In the modern days the data is coming from various devices like mobiles, tabs, laptops, computers, IOT devices e.tc and is stored in the electronics form in the systems using the concept of files or data bases. Clustering Technique is used to extract data from these sources. Improving the accuracy of the model is improved using the feature selection or feature engineering or both. The feature engineering is used to add a new feature based on the global mean. Along with that new feature in addition to the feature selection increase the overall accuracy of the model.

Clustering; Clustering Algorithms; Distance metrics; classification; Feature selection; Feature Engineering; Accuracy; Confusion matrix

Computer Science and Mathematics, Computer Science

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Global Mean Based Nearest Feature Object Value Selection with Feature Creation Method for Clustering Accuracy Improvement

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