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Service-Oriented Computing (SOC) activates communication through web services to provide computing as a service for business applications in the Service-Oriented Architecture (SOA). To make SOC successful, finding a needed service to build a system directly depending on the collection of services is a critical confront, in this paper we planned the clustering-based approach called Dynamic Clustering (DCLUS). The novelty in DCLUS compared to static-based clustering technique is the use of dynamic clustering technique. In existing CLUS, the static various widths clustering method is exploited for the users and services clustering. However, due to the limitations of static clustering, we proposed dynamic clustering to optimize the performance of clustering using data mining to find the associations and patterns, for services, and also the prediction accuracy. The performance of the proposed DCLUS system will be implemented and evaluated facing the existing system in phases of precision, recall, and f-score performance metrics using the research dataset.

Normally web services are classified originate in on the quality of service, wherever the term quality is not absolute and it is a relative term. The quality of web services is measured or derived using various parameters like reliability, scalability, flexibility, availability, etc. However, the limitation of these methods is that they are producing similar web services in recommendation lists some times. To address this research problem, the novel improved the Clustering-based web service recommendation method is proposed in this project. This approach is mainly dealing to produce diversity in the results of web service recommendation. In this method, functional interest, QoS preference, and diversity features are combined to produce the unique recommendation list of web services to end-users. To produce the unique recommendation results, we proposed a vary web service classify order that is clustering-based on web services' functional relevance such as non-useful pertinence, recorded client intrigue importance, potential client intrigue significance, etc.