Comparison of distance metrics for hierarchical data in medical databases

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

A tree-based measure for hierarchical data in mixed databases

The structure of the data in a mixed database can be a barrier when clustering that database into meaningful groups. A hierarchically structured database necessitates efficient distance measures and clustering algorithms to locate similarities between data objects. Therefore, existing literature proposes hierarchical distance measures to measure the similarities between the records in hierarchical databases. The main contribution of this research is to create and test a new distance measure for large hierarchical databases consisting of mixed data types and attributes, based on an existing tree-based (hierarchical) distance metric, the pq-gram distance metric. Several aims and objectives were pursued to fill a number of gaps in the current body of knowledge. One of these goals was to verify the validity of the pq-gram distance metric when applied to different data sets, and to compare and combine it with a number of different distance measures to demonstrate its usefulness across large mixed databases. To achieve this, further work focused on exploring how to exploit the existing method as a measure of hierarchical data attributes in mixed data sets, and to ascertain whether the new method would produce better results with large mixed databases. For evaluation purposes, the pq-gram metric was applied to The Health Improvement Network (THIN) database to determine if it could identify similarities between the records in the database. After this, it was applied to mixed data to examine different distance measures, which include non-hierarchical and other hierarchical measures, and to combine them to create a Combined Distance Function (CDF). The CDF improved the results when applied to different data sets, such as the hierarchical National Bureau of Economic Research of United States (NBER US) Patent data set and the mixed (THIN) data set. The CDF was then modified to create a New-CDF, which used only the hierarchical pq-gram metric to measure the hierarchical attributes in the mixed data set. The New-CDF worked well, finding the most similar data records when applied to the THIN data set, and grouping them in one cluster using the Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) clustering algorithm. The quality of the clusters was explored using two internal validation indices, Silhouette and C-Index, where the values showed good compactness and quality of the clusters obtained using the new method

Comparison of distance metrics for hierarchical data in medical databases

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

A tree-based measure for hierarchical data in mixed databases

The structure of the data in a mixed database can be a barrier when clustering that database into meaningful groups. A hierarchically structured database necessitates efficient distance measures and clustering algorithms to locate similarities between data objects. Therefore, existing literature proposes hierarchical distance measures to measure the similarities between the records in hierarchical databases. The main contribution of this research is to create and test a new distance measure for large hierarchical databases consisting of mixed data types and attributes, based on an existing tree-based (hierarchical) distance metric, the pq-gram distance metric. Several aims and objectives were pursued to fill a number of gaps in the current body of knowledge. One of these goals was to verify the validity of the pq-gram distance metric when applied to different data sets, and to compare and combine it with a number of different distance measures to demonstrate its usefulness across large mixed databases. To achieve this, further work focused on exploring how to exploit the existing method as a measure of hierarchical data attributes in mixed data sets, and to ascertain whether the new method would produce better results with large mixed databases. For evaluation purposes, the pq-gram metric was applied to The Health Improvement Network (THIN) database to determine if it could identify similarities between the records in the database. After this, it was applied to mixed data to examine different distance measures, which include non-hierarchical and other hierarchical measures, and to combine them to create a Combined Distance Function (CDF). The CDF improved the results when applied to different data sets, such as the hierarchical National Bureau of Economic Research of United States (NBER US) Patent data set and the mixed (THIN) data set. The CDF was then modified to create a New-CDF, which used only the hierarchical pq-gram metric to measure the hierarchical attributes in the mixed data set. The New-CDF worked well, finding the most similar data records when applied to the THIN data set, and grouping them in one cluster using the Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) clustering algorithm. The quality of the clusters was explored using two internal validation indices, Silhouette and C-Index, where the values showed good compactness and quality of the clusters obtained using the new method

Ascertainment of landscape values in a municipality with extended competence České Budějovice

Submitted thesis deals with the area of a municipality with extended competence of České Budějovice that is a part of a district with the same name in the South Bohemian Region. The aim of this thesis is the completion of the contemporary territorially-analytical data in cooperation with the Regional Authority of the South Bohemian Region in České Budějovice and providing the results of this research to relevant authorities. The thesis consists of three main parts: theoretical, which deals with the characteristics of the area and territorially-analytical sources, methodological, describing the data collection and creating the map materials, and description of discovered values of the landscape. The main outcomes are map layers and attribute tables, capturing discovered values of the landscape, attached to them