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.