<![CDATA[This paper proposed a novel variation of spectral clustering model based on a novel affinitymetric that considers the distribution of the neighboring points to learn the underlayingstructures in the data set. Proposed affinity metric is calculated using Mahalanobis distancethat exploits the concept of outlier detection for identifying the neighborhoods of the datapoints. RandomWalk Laplacian of the representative graph and its spectra has been consideredfor the clustering purpose and the first k number of eigenvectors have been consideredin the second phase of clustering. The model has been tested with benchmark data and thequality of the output of the proposed model has been tested in various clustering indicesscales.]]>
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