Background/Objectives: In Software Engineering discipline, requirement phase plays vital role in success of a project. To make the requirement analysis process more effective, developers adopt several methods like goal-oriented, aspectoriented, pattern-oriented etc. In the current scenario recurring requirements can be addressed effectively with requirement patterns rather applying a common procedure to all projects. Methods/Statistical Analysis: While adopting common procedure to all project results in increasing cost of quality or even project failure due to the fact that every project is not having similar characteristics. To perform requirement process effectively, it is necessary to select a suitable requirement pattern for a project. Selecting relevant, applicable and reliable pattern is challengeable and prior knowledge is required. Findings: The proposed framework groups project characteristics and the pattern sets into k-clusters based on the clustering algorithm. The best suitable pattern will be filtered from the available pattern clusters by applying the filtering algorithm. The suggested patterns along with the situational constraints for a given situation will be stored in the data store. The stack holder can add new pattern details or can update the details of existing patterns Applications/ Improvements: Finds the analysis patterns that match with the given situational characteristics and requirement of a project. Clustering enhanced with fuzzy to bring the improvement.