Internal Cluster Validation for Data with Missing Values

Clustering is an unsupervised data mining method used to label data into distinct groups. It has numerous applications in various fields, from bioinformatics to object recognition and categorization. The prototype-based clustering methods summarize information in form of cluster centroids that are often called as prototypes. Cluster validation methodology provides a means of assessing the goodness of a clustering solution and identify the optimal number of clusters in the data. Internal cluster validation methods evaluate the quality of clustering by assessing the cluster compactness and separability on the same data set that is input in the clustering phase. A common and sometimes complex issue for both data clustering and cluster validation is the presence of missing values in data that can occur for many different causes, such as non-respondents in questionnaire studies or device operation failures. This dissertation focuses on extending cluster validation models for treating missing values on data. Since these models are not based on the values of the data vectors but on the computed distances between these vectors, missing value treatment is covered by direct distance estimation between data vectors. The thesis presents a toolbox that is used to demonstrate the usability of the developed methods for research and development purposes. In addition, the background theory of each element of the toolbox and use case examples are proposed. A real-world application is provided where cluster validation is utilized for categorizing learning game players into distinct profiles using a gameplay data in which a part of data values are missing. As the main outcome of the thesis, the missing value handling methods for data preprocessing, clustering, and cluster validation are presented. The functionality and validity of the methods are demonstrated using several numerical experiments and the results confirms the scalability of the techniques and their capability of reliably solving knowledge discovery problems. Keywords: knowledge discovery, data mining, log data, data preprocessing, missing values, distance computation, distance estimation, clustering, protype-based clustering, number of clusters, cluster validation, internal cluster validation, cluster validation indices