Comparison of distance metrics for hierarchical data in medical databases

Diman Hassan, Uwe Aickelin, Christian Wagner

Research output: Contribution to conferencePaper

4 Citations (Scopus)


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.
Original languageEnglish
Publication statusPublished - 15 Mar 2014
Event2014 International Joint Conference on Neural Networks (IJCNN) - Beijing, China
Duration: 6 Jul 201411 Jul 2014


Conference2014 International Joint Conference on Neural Networks (IJCNN)


  • Data Mining, Database Management Systems, Medical Information Systems, Pattern Clustering


Dive into the research topics of 'Comparison of distance metrics for hierarchical data in medical databases'. Together they form a unique fingerprint.

Cite this