Clustering is one of the widely used data mining techniques and most important un-supervised learning technique. Most of the clustering methods group data based on distance and few methods cluster data based on similarity. In this paper, similarity relationship among relational input data with similar expression patterns are considered so that a consequential and simple analytical decision can be made from the proposed Hierarchical Fuzzy RElational K-means Clustering Algorithm (HFRECCA). This paper is an extension of FRECCA which is used for the clustering of text data. HFRECCA and K-means clustering algorithm are compared with each other on the basis of various parameters such as entropy, purity and V-measure. Experimental results prove that the proposed method is better than the existing method.