Comparison of distance metrics for hierarchical data in medical databases
Hassan, Diman; Aickelin, Uwe; Wagner, Christian
CHRISTIAN WAGNER Christian.Wagner@nottingham.ac.uk
Professor of Computer Science
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 pg-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 pogram 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.
Hassan, D., Aickelin, U., & Wagner, C. (2014). Comparison of distance metrics for hierarchical data in medical databases.
|Conference Name||2014 International Joint Conference on Neural Networks (IJCNN)|
|Start Date||Jul 6, 2014|
|End Date||Jul 11, 2014|
|Acceptance Date||Mar 15, 2014|
|Publication Date||Sep 4, 2014|
|Deposit Date||Sep 30, 2014|
|Publicly Available Date||Sep 30, 2014|
|Peer Reviewed||Peer Reviewed|
|Keywords||Biomedical Informatics, Data Mining, Machine Learning|
|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.|
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