A Cluster-Based Approach to Improve Similarity-Based Retrieval for Process-Oriented Case-Based Reasoning

In case-based reasoning, improving the performance of the retrieval phase is still an important research issue for complex case representations and computationally expensive similarity measures. This holds particularly for the of retrieval workflows, which is a recent topic in process-oriented case-based reasoning. While most index-based retrieval methods are restricted to attribute-value representations, the application of a MAC/FAC retrieval approach introduces significant additional domain-specific development effort due to design the MAC phase. In this paper, we present a new indexbased retrieval algorithm, which is applicable beyond attribute-value representations without introducing additional domain-specific development effort. It consists of a new clustering algorithm that constructs a cluster-based index structure based on case similarity, which helps finding the most similar cases more efficiently. The approach is developed and analyzed for the retrieval of semantic workflows. It significantly improves the retrieval time compared to a linear retriever, while maintaining a high retrieval quality. Further, it achieves a similar performance than the MAC/FAC retriever if the case base has a cluster structure, i.e., if it contains groups of similar cases.