Abstract: Clustering is one of the effective techniques that separate the data into meaningful groups. Feature selection is an important concept to enhance efficiency in clustering process. Existing work presented a method called hybridization of K-means algorithm and Harmony Search Method (HSM) for clustering the documents. In this method, concept factorization is used to extract the meanings to cluster the documents. But it needs to improve clustering accuracy in the document clustering process. In this manuscript, Kernel and Weighted feature based Clustering (KWC) method is presented to cluster the documents. Spherical kernel is utilized as the higher order kernel that is higher rate of computation. Furthermore, the weight of each concept is calculated and select as the weighted features. The problem in this method is poor generalization performance so it needs to select optimal kernel matrix. So, Particle Swarm Optimization (PSO) based Optimal Kernel Matrix Selection (PSO-OKMS) is presented to select the optimal value of kernel matrix. In this method, kernel set is to chosen accurately to improve clustering performance but the accuracy is less. Furthermore Unsupervised Constrained based Hybrid Clustering (UC-HC) to improve the clustering performance. In this method, data are extracted by identifying an assignment that rises similarity score between strings and informs to the constraints. Experimental result compares methods such as KWC, PSO-OKMS and UC-HC to measure the clustering accuracy. The proposed UC-HC method shows high accuracy when compared to KWC and PSO-OKMS methods.