This thesis explores the feasibility of clustering mixed data and unevenly spaced time series for customer segmentation. The proposed method implements the Gower dissimilarity as the local distance function in dynamic time warping to calculate dissimilarities between mixed data time series. The time series are then clustered with k−medoids and the clusters are evaluated with the silhouette score and t−SNE. The study further investigates the use of a time warping regularisation parameter. It is derived that implementing time as a feature has the same effect as penalising time warping, andtherefore time is implemented as a feature where the feature weight is equivalent to a regularisation parameter. The results show that the proposed method successfully identifies clusters in customer transaction data provided by Nordea. Furthermore, the results show a decrease in the silhouette score with an increase in the regularisation parameter, suggesting that the time at which a transaction occurred might not be of relevance to the given dataset. However, due to the method’s high computational complexity, it is limited to relatively small datasets and therefore a need exists for a more scalable and efficient clustering technique. / Denna uppsats utforskar klustring av ojämnt fördelade tidsserier med numeriska och kategoriska variabler för kundsegmentering. Den föreslagna metoden implementerar Gower dissimilaritet som avståndsfunktionen i dynamic time warping för att beräkna dissimilaritet mellan tidsserierna. Tidsserierna klustras sedan med k-medoids och klustren utvärderas med silhouette score och t-SNE. Studien undersökte vidare användningen av en regulariserings parameter. Det härledes att implementering av tid som en egenskap hade samma effekt som att bestraffa dynamic time warping, och därför implementerades tid som en egenskap där dess vikt är ekvivalent med en regulariseringsparameter.  Resultaten visade att den föreslagna metoden lyckades identifiera kluster i transaktionsdata från Nordea. Vidare visades det att silhouette score minskade då regulariseringsparametern ökade, vilket antyder att tiden transaktion då en transaktion sker inte är relevant för det givna datan. Det visade sig ytterligare att metoden är begränsad till reltaivt små dataset på grund av dess höga beräkningskomplexitet, och därför finns det behov av att utforksa en mer skalbar och effektiv klusteringsteknik.

In this thesis, the main focus is to formulate and test a suitable model forexogenous fault detection in swarms containing unmanned aerial vehicles(UAVs), which are aerial autonomous systems. FOI Swedish DefenseResearch Agency provided the thesis project and research question. Inspiredby previous work, the implementation use behavioral feature vectors (BFVs)to simulate the movements of the UAVs and to identify anomalies in theirbehaviors. The chosen algorithm for fault detection is the density-based cluster analysismethod known as the Local Outlier Factor (LOF). This method is built on thek-Nearest Neighbor(kNN) algorithm and employs densities to detect outliers.In this thesis, it is implemented to detect faulty agents within the swarm basedon their behavior. A confusion matrix and some associated equations are usedto evaluate the accuracy of the method. Six features are selected for examination in the LOF algorithm. The firsttwo features assess the number of neighbors in a circle around the agent,while the others consider traversed distance, height, velocity, and rotation.Three different fault types are implemented and induced in one of the agentswithin the swarm. The first two faults are motor failures, and the last oneis a sensor failure. The algorithm is successfully implemented, and theevaluation of the faults is conducted using three different metrics. Several setsof experiments are performed to assess the optimal value for the LOF thresholdand to understand the model’s performance. The thesis work results in a strongLOF value which yields an acceptable F1 score, signifying the accuracy of theimplementation is at a satisfactory level. / I denna uppsats är huvudfokuset att formulera och testa en lämplig modellför detektion av exogena fel i svärmar som innehåller obemannade flygfordon(UAV:er), vilka utgör autonoma luftburna system. Examensarbetet ochforskningsfrågan tillhandahölls av FOI, Totalförsvarets forskningsinstitut.Inspirerad av tidigare arbete används beteendemässiga egenskapsvektorer(BFV:er) för att simulera rörelserna hos UAV:erna och för att identifieraavvikelser i deras beteenden. Den valda algoritmen för felavkänning är en densitetsbaserad klusterana-lysmetod som kallas Local Outlier Factor (LOF). Denna metod byggerpå k-Nearest Neighbor-algoritmen och använder densiteter för att upptäckaavvikande datapunkter. I denna uppsats implementeras den för att detekterafelaktiga agenter inom svärmen baserat på deras beteende. En förväxlings-matris(Confusion Matrix) och dess tillhörande ekvationer används för attutvärdera metodens noggrannhet. Sex egenskaper valdes för undersökning i LOF-algoritmen. De första tvåegenskaperna bedömer antalet grannar i en cirkel runt agenter, medande andra beaktar avstånd, höjd, hastighet och rotation. Tre olika feltyperimplementeras och framkallas hos en av agenterna inom svärmen. De förstatvå felen är motorfel, och det sista är ett sensorfel. Algoritmen implementerasframgångsrikt och utvärderingen av felen genomförs med hjälp av treolika mått. Ett antal uppsättningar av experiment utförs för att hitta detoptimala värdet för LOF-gränsen och för att förstå modellens prestanda.Examensarbetet resultat är ett optimalt LOF-värde som genererar ettacceptabelt F1-score, vilket innebär att noggrannheten för implementationennår en tillfredsställande nivå.

This thesis explores how dental students experience their education. We aim to generate ways to understand which elements relate to the students’ experience based on current theories and models regarding the quality of working life and gender (and) power relations.   Methods Twelve interviews with Umeå dental students in their clinical semesters were analysed with a Grounded Theory (GT) as well as a content analysis approach. A web-survey was sent to all clinical dental students in Sweden (P ≈ 805) with a response rate of 40% (p = 322). The quantitative methods included structural equation modelling and cluster analysis. Results The GT analysis resulted in the core category “Experiencing ambiguity,” that captured the student’s role-ambiguity. Central categories focused on perceived stress and performance assessment in relation to ambiguous inner and outer demands. The content analysis resulted in three categories: “Notions of inequalities,” “Gendering,” and “The student position.” These categories present the ways groups of students are constructed in relation to the student/dentist norm and social gender relations, and how women and men of foreign descent risk subordination and stereotyping. The SEM-model contained psychosocial work environment, tolerance for ambiguity, perceived stress, and student satisfaction. Work environment influenced both perceived stress and satisfaction, and stood for almost all of the explained variance in perceived stress for women, indicating that women are constructed as co-responsible for the work environment. About half of the variance for the men was explained by tolerance for ambiguity, indicating that the feeling of uncertainty may lead to stress in men who include “being in control” in their gender identity. The cluster analysis resulted in a six-cluster solution ranging from “The fresh and positive” to “The worn critiques.” Psychosocial work environment again appeared to be the main factor. Gender also appears to be a factor as the gender distribution in the best as well as the two worst clusters differs from the population. Conclusion Work environment stands out among the factors that relate to the students wellbeing and satisfaction, but the student group is heterogeneous and the ways students perceive their work environment relate to different processes and experiences. We suggest that the ways gender and ethnicity appear to be constructed in relation to the sociocultural gender power relations and the (traditional) medical hierarchy could be of importance for how the students’ experience their psychosocial work environment.