Continuously increasing amount of logged data increases the possibilities of finding new discoveries about the user interaction with the application for which the data is logged. Traces from the data may reveal some specific user behavioural patterns which can discover how to improve the development of the application by showing the ways in which the application is utilized. In this thesis, unsupervised machine learning techniques are used in order to group the users depending on their utilization of SEB Privat Android mobile application. The user interactions in the applications are first extracted, then various data preprocessing techniques are implemented to prepare the data for clustering and finally two clustering algorithms, namely, HDBSCAN and KMedoids are performed to cluster the data. Three types of user behaviour have been found from both K-medoids and HDBSCAN algorithm. There are users that tend to interact more with the application and navigate through its deeper layers, then the ones that consider only a quick check of their account balance or transaction, and finally regular users. Among the resulting features chosen with the help of feature selection methods, 73 % of them are related to user behaviour. The findings can be used by the developers to improve the user interface and overall functionalities of application. The user flow can thus be optimized according to the patterns in which the users tend to navigate through the application. / En ständigt växande datamängd ökar möjligheterna att hitta nya upptäckter om användningen av en mobil applikation för vilken data är loggad. Spår som visas i data kan avslöja vissa specifika användarbeteenden som kan förbättra applikationens utveckling genom att antyda hur applikationen används. I detta examensarbete används oövervakade maskininlärningstekniker för att gruppera användarna beroende på deras bruk av SEB Privat Android mobilapplikation. Användarinteraktionerna i applikationen extraheras ut först, sedan används olika databearbetningstekniker för att förbereda data för klustringen och slutligen utförs två klustringsalgoritmer, nämligen HDBSCAN och Kmedoids för att gruppera data. Tre distinkta typer av användarbeteende har hittats från både K-medoids och HDBSCAN-algoritmen. Det finns användare som har en tendens att interagera mer med applikationen och navigera genom sitt djupare lager, sedan finns det de som endast snabbt kollar på deras kontosaldo eller transaktioner och till slut finns det vanliga användare. Bland de resulterande attributen som hade valts med hjälp av teknikerna för val av attribut, är 73% av dem relaterade till användarbeteendet. Det som upptäcktes i denna avhandling kan användas för att utvecklarna ska kunna förbättra användargränssnittet och övergripande funktioner i applikationen. Användarflödet kan därmed optimeras med hänsyn till de sätt enligt vilka användarna har en speciell tendens att navigera genom applikationen.

The thesis deals with text mining. It describes the theory of text document clustering as well as algorithms used for clustering. This theory serves as a basis for developing an application for clustering text data. The application is developed in Java programming language and contains three methods used for clustering. The user can choose which method will be used for clustering the collection of documents. The implemented methods are K medoids, BiSec K medoids, and SOM (self-organization maps). The application also includes a validation set, which was specially created for the diploma thesis and it is used for testing the algorithms. Finally, the algorithms are compared according to obtained results.

This study deals with a municipal solid waste management system in which the strategic and tactical decisions are addressed simultaneously. In the system, the number and locations of the transfer facilities which serve to the particular solid waste pick-up points and the landfill are determined. Additionally, routing plans are constructed for the vehicles which collect the solid waste from the pick-up points by regarding the load capacity of the vehicles and shift time restrictions. We formulate this reverse logistics system as a location-routing problem with two facility layers. Mathematical models of the problem are presented, and an iterative capacitated-k-medoids clustering-based heuristic method is proposed for the solution of the problem. Also, a sequential clustering-based heuristic method is presented as a benchmark to the iterative method. Computational studies are performed for both methods on the problem instances including up to 1000 pick-up points, 5 alternative transfer facility sites, and 25 vehicles. The results obtained show that the iterative clustering-based method developed achieves considerable improvement over the sequential clustering-based method.

The master' s thesis deals with cluster data analysis. There are explained basic concepts and methods from this domain. Result of the thesis is Cluster analysis tool, in which are implemented methods K-Medoids and DBSCAN. Adjusted results on real data are compared with programs Rapid Miner and SAS Enterprise Miner.

Made available in DSpace on 2016-06-02T20:06:03Z (GMT). No. of bitstreams: 1 2596.pdf: 1631367 bytes, checksum: 90f2d842a935f1dd50bf587a33f6a2cb (MD5) Previous issue date: 2009-02-16 / The clustering techniques have frequently been used in literature to the analyse data in several fields of application. The main objective of this work is to study such techniques. There is a large number of clustering techniques in literature. In this work we concentrate on Self Organizing Map (SOM), k-means, k-medoids and Expectation- Maximization (EM) algorithms. These algorithms are applied to gene expression data. The analisys of gene expression, among other possibilities, identifies which genes are differently expressed in synthesis of proteins associated to normal and sick tissues. The purpose is to do a comparing of these metods, sticking out advantages and disadvantages of such. The metods were tested for simulation and after we apply them to a real data set. / As técnicas de agrupamento (clustering) vêm sendo utilizadas com freqüência na literatura para a solução de vários problemas de aplicações práticas em diversas áreas do conhecimento. O principal objetivo deste trabalho é estudar tais técnicas. Mais especificamente, estudamos os algoritmos Self Organizing Map (SOM), k-means, k-medoids, Expectation-Maximization (EM). Estes algoritmos foram aplicados a dados de expressão gênica. A análise de expressão gênica visa, entre outras possibilidades, a identificação de quais genes estão diferentemente expressos na sintetização de proteínas associados a tecidos normais e doentes. O objetivo deste trabalho é comparar estes métodos no que se refere à eficiência dos mesmos na identificação de grupos de elementos similares, ressaltando vantagens e desvantagens de cada um. Os métodos foram testados por simulação e depois aplicamos as metodologias a um conjunto de dados reais.

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This master's thesis consider clustering of protein sequences based on primary structure of proteins. Studies the protein sequences from they primary structure. Describes methods for similarities in the amino acid sequences of proteins, cluster analysis and clustering algorithms. This thesis presents concept of distance function based on similarity of protein sequences and implements clustering algorithms ANGES, k-means, k-medoids in Python programming language.