Comparison of distance metrics for hierarchical data in medical databases

Hassan, Diman and Aickelin, Uwe and Wagner, Christian (2014) Comparison of distance metrics for hierarchical data in medical databases. In: 2014 International Joint Conference on Neural Networks (IJCNN), 6-11 July 2014, Beijing, China.

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Distance metrics are broadly used in different research areas and applications, such as bio- informatics, data mining and many other fields. However, there are some metrics, like pq-gram and Edit Distance used specifically for data with a hierarchical structure. Other metrics used for non- hierarchical data are the geometric and Hamming metrics. We have applied these metrics to The Health Improvement Network (THIN) database which has some hierarchical data. The THIN data has to be converted into a tree-like structure for the first group of metrics. For the second group of metrics, the data are converted into a frequency table or matrix, then for all metrics, all distances are found and normalised. Based on this particular data set, our research question: which of these metrics is useful for THIN data? This paper compares the metrics, particularly the pqgram metric on finding the similarities of patients’ data. It also investigates the similar patients who have the same close distances as well as the metrics suitability for clustering the whole patient population. Our results show that the two groups of metrics perform differently as they represent different structures of the data. Nevertheless, all the metrics could represent some similar data of patients as well as discriminate sufficiently well in clustering the patient population using k-means clustering algorithm.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published in: Proceedings of the 2014 International Joint Conference on Neural Networks : July 6-11, 2014, Beijing, China. Piscataway, N.J. : IEEE, 2014. ISBN: 978-1-4799-1484-5. pp. 3636-3643, doi: 10.1109/IJCNN.2014.6889554 ©2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: Data Mining, Database Management Systems, Medical Information Systems, Pattern Clustering
Schools/Departments: University of Nottingham, Ningbo Campus > Faculty of Science and Engineering > Division of Computer Science
University of Nottingham UK Campus > Faculty of Science > School of Computer Science
Depositing User: Aickelin, Professor Uwe
Date Deposited: 17 Jun 2016 13:33
Last Modified: 14 Sep 2016 08:59

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