Context rank based hierarchical clustering algorithm on medical databases (CRBHCA).

Bano, Shahana; Rao, K. Rajasekhara

Abstract

In this paper we propose a method that aims to reduce processing overheads by avoiding the need to choose between natural language processing tools such as part-of-speech taggers and parsers. Moreover, we suggest a structure for the immediate creation of a large-scale, annotated corpus with disease names, which can be applied to train our probabilistic model. In this proposed work, a context rank-based hierarchical clustering method is applied on different datasets relating to colon diseases, leukemia, mixed-lineage leukemia (MLL) and lymphoma medical diseases. An optimal rule-filtering algorithm is applied on these datasets to remove unwanted special characters for gene/protein identification. Finally, experimental results show that our proposed method outperformed existing methods in terms of time and clusters space.

Citation

BANO, S. and RAO, K.R. 2015. Context rank based hierarchical clustering algorithm on medical databases (CRBHCA). Journal of theoretical and applied information technology [online], 75(2), pages 199-211. Available from: https://www.jatit.org/volumes/Vol75No2/10Vol75No2.pdf

• Journal Article Type: Article
• Acceptance Date: May 20, 2015
• Online Publication Date: May 20, 2015
• Publication Date: May 20, 2015
• Deposit Date: Sep 22, 2023
• Publicly Available Date: Sep 22, 2023
• Journal: Journal of theoretical and applied information technology
• Print ISSN: 1992-8645
• Electronic ISSN: 1817-3195
• Publisher: Journal of Theoretical and Applied Information Technology
• Peer Reviewed: Peer Reviewed
• Volume: 75
• Issue: 2
• Pages: 199-211
• Keywords: Data clustering; Biomedical data; Gene sequencing; Protein sequencing; Leukemia; Machine learning
• Public URL: https://rgu-repository.worktribe.com/output/2064142