  Spectral clustering is a key research topic in the field of machine learning
and data mining. Most of the existing spectral clustering algorithms are built
upon Gaussian Laplacian matrices, which are sensitive to parameters. We propose
a novel parameter free, distance consistent Locally Linear Embedding. The
proposed distance consistent LLE promises that edges between closer data points
have greater weight.Furthermore, we propose a novel improved spectral
clustering via embedded label propagation. Our algorithm is built upon two
advancements of the state of the art:1) label propagation,which propagates a
node\'s labels to neighboring nodes according to their proximity; and 2)
manifold learning, which has been widely used in its capacity to leverage the
manifold structure of data points. First we perform standard spectral
clustering on original data and assign each cluster to k nearest data points.
Next, we propagate labels through dense, unlabeled data regions. Extensive
experiments with various datasets validate the superiority of the proposed
algorithm compared to current state of the art spectral algorithms.
