With the continuous progress of information technology, people can use more and more diversified and complex ways to describe things more accurately, which leads to the emergence of multi-view data. Clustering multi-view data is a fundamental and important topic in data mining, machine learning, pattern recognition and other fields. In this era of information explosion, the dimension of data is higher and higher. How to efficiently cluster this kind of data remains a huge challenge. In order to solve the current k-means when dealing with high-dimensional data are multiple views of the shortage problem of ability, this paper proposes a new multiview clustering framework, called the weighted k-means algorithm are multiple views (Self - weighted Multi - view K - means algorithm, SwMKM). First of all, through the adoption of least absolute principles to guide the robustness, this method successfully reduce the effects of outliers on the results. Then, the iterative reweighted least square method is used to solve the minimum absolute residual, and the distribution of multiple weights is adjusted adaptively to achieve the reweighting control. Finally, by introducing a projection matrix with a 2, 1-norm penalty term, the high-dimensional feature space of the original dataset is transformed into a statistically uncorrelated, low-dimensional subspace for feature selection and noise suppression. Experimental results show that the performance on Handwritten numerals, MSRCv1, Outdoor Scene and other datasets is significantly better than other multi-view K-means methods, which proves the superiority of the algorithm.