An Improved Semi-supervised Clustering Algorithm Based on Active Learning

In order to solve the difficult questions such as in the presence of the cluster deviation and high dimensional data processing in traditional semi-supervised clustering algorithm, a semi-supervised clustering algorithm based on active learning was proposed, this algorithm can effectively solve the above two problems. Using active learning strategies in algorithm can obtain a large amount of information of pairwise constraints therefore enhance the proportion of prior knowledge. And the use of this constraint set projection space, finally in the mapping of the subspace, the improved K-means algorithm implemented for data clustering, as the algorithm clustering object is a low dimensional data, and prior knowledge increased, clustering in time efficiency can be guaranteed, and also can solve the deviation problem of clustering. The experiment results show that, with active learning algorithm clustering performance improvement, was superior to the other two semi-supervised clustering algorithms.