Landmark-based Spectral Clustering (LSC) is used for large scale spectral clustering. The basic idea of the our approach is designing an efficient way for graph construction. K nearest neighbors is a simple algorithm that stores all available cases and classifies new cases based on a similarity measure like distance functions. k-NN is a type of instance-based learning, or lazy learning. In this field, the CRF approach is relatively novel and considered a prominent choice as compared to other methods. However, a lot of scope for further enhancement of the CRF(Conditional Random Field) with Knn optimization problem. The Performance of CRF-Knn has shown quite significant resultsand using different datasets in this paper.The proposed technique improves the selection process using KNN algorithm. The results obtained show that the CRF found to be better than that of LSC in terms of Accuracy, time, recall and precision.

Landmark-based Spectral clustering, K nearest neighbors, CRF, Accuracy, time, precision and recall

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