Data Fusion in K-Means Laplacian Clustering

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Provided by: International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE)
Topic: Data Management
Format: PDF
In clustering technique, the hard clustering membership values and overlapping concept could not be identified along with non-convex problem. The proposed algorithm uses soft clustering to combine both laplacians and multiple kernels for clustering analysis. The algorithm is formulated on a rayleigh quotient objective function. The bi-level optimization is an alternating minimization procedure; it is used to convert the hard clustering to soft clustering. The kernels and laplacians co-efficient can be optimized automatically by using the methods semi-infinite programming and quadratic constraint quadratic programming.
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