In many online forms, lots of side-data or meta information is available. This Meta data consists of different kinds, for example the links present in the file, the user-access performance from blogs, the document origin information, and also other attributes which are surrounded into the content or text document. For the clustering purposes, these Meta attributes contain large amount of information. The Meta data adds the noise to the mining process. So, it is difficult to incorporate into this process. The existing COATES algorithm is created for clustering approach. But, in COATES the kmeans algorithm creates some problems as it is unable to get the quality of clusters better. Because, it leads to the wrong number of clusters, different sized clusters, and empty clusters and outliers. The authors have proposed a Hybrid-COATES algorithm which combines CURE with COATES algorithm for an efficiency and effective clustering approach. To mine text data with the help of Meta data or side information, CURE algorithm is more capable than kmeans algorithm. Hybrid-COATES method is used to attempt to the scalability problem and improve the quality of clustering results.