In this paper

we propose a novel weighted clustering algorithm based on Laplacian weight

which can automatically transform the structure information between clustering objects into weights of objects.Because Laplacian weight can indicate the neighborhood structure of original data set

better clustering is achieved.Performed on conventional C-means or fuzzy C-means methods

the proposed Laplacian weighting scheme can effectively improve the clustering performance.In addition

the new algorithm achieves some extra advantages such as robustness to outliers

suitability for class-imbalance data clustering and insensitivity to number of clusters

etc.Experimental results on artificial datasets and UCI machine learning repository validate the effectiveness of the proposed algorithm.