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Pre-Clustering Techniques for Healthcare System: Evaluation Measures, Evaluation Metrics, Comparative Study of Existing vs. Proposed Approaches
Abstract
This chapter presents a comparative study of the proposed approaches (i.e., extended dark block extraction [EDBE], extended cluster count extraction [ECCE], and extended co-VAT approaches). This chapter evaluates pre-clustering and post-clustering algorithms on real-time data and synthetic datasets. Unlike traditional clustering algorithms, pre-clustering algorithms provide a prior clustering on different datasets. Simulation studies are carried out using datasets having both class-labeled and unlabeled information. Comparative studies are performed between results of existing pre-clustering and proposed pre-clustering approaches. A simulated RDI-based preprocessing method is also applied for data diversification. Extensive simulation on real and synthetic datasets shows that pre-clustering algorithms with simulated RDI-based pre-processing performs better compared to conventional post-clustering algorithms.
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