Using MCDM Technique to Identify Optimal Incremental Clustering Algorithm
摘要
This conference paper presents a novel Analytic Hierarchy Process (AHP) technique for identifying the optimal incremental clustering algorithm. Large datasets that cannot be processed in a single batch are handled by incremental clustering algorithms by processing data points incrementally and updating the clustering model. However, selecting the best incremental clustering algorithm can be challenging due to the many available options and conflicting evaluation criteria. The proposed AHP technique considers clustering quality, processing time, and scalability as criteria for evaluating the performance of incremental clustering algorithms.