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Optimization and Scalable Constrained Clustering Performances
Objectives: To achieve the accuracy of clustering performances higher and to optimize the scalable approaches.
Methods: Constrained spectral clustering and optimization algorithms are used to analyze and evaluate the large dataset. It is used to produce quality of clustering results.
Findings: The proposed method achieves high performance in terms of precision, recall and accuracy.
Application/Improvements: The proposed system is done by using optimization algorithm and pairwise constraints concepts. The optimization algorithm is used to increase the clustering accuracy and produce more optimal performances.
Keywords
Constrained Spectral Clustering, Scalability and Optimization.
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