Clustering has become an increasingly important task in modern application domains. Targeting useful and relevant information on the World Wide Web is a topical and highly complicated research area. Clustering techniques have been applied to categorize documents on web and extracting knowledge from the web. In this paper we propose novel clustering algorithms based on Harmony Search (HS) optimization method that deals with web document clustering. By modeling clustering as an optimization problem, first, we propose a pure HS based clustering algorithm that finds near global optimal clusters within a reasonable time. Then we hybridize K-means and harmony clustering to achieve better clustering. Experimental results on five different data sets reveal that the proposed algorithms can find better clusters when compared to similar methods and the quality of clusters is comparable. Also proposed algorithms converge to the best known optimum faster than other methods.