This paper presents a detailed study of different clustering based image clustering algorithm. A cluster is collection or group of data objects that are similar to each other with the same cluster object and not similar with other cluster object. Also it is study on different fuzzy rule based clustering algorithm. To overcome the limitations of conventional FCM its need to study Kernel fuzzy c-means (KFCM) algorithm in detail. Basic K-means algorithm is sensitive to noise and outliers so, and changes of K-means called as Fuzzy c- means (FCM) are developed .FCM allows data points to belong to number of cluster where each data point has own degree of membership of belonging to each cluster. The KFCM uses a unique function and gives better performance than FCM in case of noise corrupted images. So it is nothing but grouping of set of physical data objects into the classes of similar or matching objects. The fuzzy rule clustering is the crisp clustering at the boundaries among the cluster are vague and ambiguous. Up to yet the cluster never can be identified by the human directly but which was possible for the machines or system to identify cluster easily as per the requirements of dataset or system. The cluster which is fuzzy in nature is quite difficult to understand. The most drawback of fuzzy and crisp clustering algorithm is there nature of sensitivity to number of potential cluster and their initial position. The image clustering is not easy to understand for human up to yet. This is concept behind of this fuzzy clustering to make it possible to understand for human, And also to make the crisp and boundaries easy for the image cluster. The accuracy of the finding image cluster should be to maintain with respective rate. This will be another attempt to make it possible by using different types of algorithm.