Performance Evaluation Of Clustering Algorithms With Constraints And Parameters

M PRASAD; Srikanth T

doi:10.20944/preprints202309.1006.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 14 September 2023 / Approved: 14 September 2023 / Online: 15 September 2023 (04:24:59 CEST)

Data Extraction is a technique is called as clustering which is used to retrieve data either from the files or data bases or both. This paper focuses on the performance evaluation parameters of the clustering algorithms based on different parameters or conditions or constraints and parameters which are used to perform the clustering process to get the clusters on the data sets. Therefore best clusters are retrieved when best parameters or conditions or constraints or preferences which are applied on the data sources for the clustering process. These parameters or conditions or constraints are opted by the user called as user preferences.

clustering; machine learning; clustering algorithms; conditions; similarity functions; clustering process; types of learning; data dimensions

Computer Science and Mathematics, Computer Science

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Performance Evaluation Of Clustering Algorithms With Constraints And Parameters

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