  Spectral Clustering(SC) is a prominent data clustering technique of recent
times which has attracted much attention from researchers. It is a highly
data-driven method and makes no strict assumptions on the structure of the data
to be clustered. One of the central pieces of spectral clustering is the
construction of an affinity matrix based on a similarity measure between data
points. The way the similarity measure is defined between data points has a
direct impact on the performance of the SC technique. Several attempts have
been made in the direction of strengthening the pairwise similarity measure to
enhance the spectral clustering. In this work, we have defined a novel affinity
measure by employing the concept of non-conformity used in Conformal
Prediction(CP) framework. The non-conformity based affinity captures the
relationship between neighborhoods of data points and has the power to
generalize the notion of contextual similarity. We have shown that this
formulation of affinity measure gives good results and compares well with the
state of the art methods.
