  Efficiently discovering relevant Web services with respect to a specific user
query has become a growing challenge owing to the incredible growth in the
field of web technologies. In previous works, different clustering models have
been used to address these issues. But, most of the traditional clustering
techniques are computationally intensive and fail to address all the problems
involved. Also, the current standards fail to incorporate the semantic
relatedness of Web services during clustering and retrieval resulting in
decreased performance. In this paper, we propose a framework for web services
retrieval that uses a bottom-up, decentralized and self organising approach to
cluster available services. It also provides online, dynamic computation of
clusters thus overcoming the drawbacks of traditional clustering methods. We
also use the semantic similarity between Web services for the clustering
process to enhance the precision and lower the recall.
