Sökning: "k-means"
Visar resultat 21 - 25 av 198 uppsatser innehållade ordet k-means.
21. Design of high performance buildings : Vulnerability of buildings to climate change from an energy perspective
Master-uppsats, KTH/Hållbara byggnaderSammanfattning : The challenge of climate change is twofold: to mitigate (prevent) the causes of climate change and to prepare (adapt) to the inevitable effects and consequences. Building and construction are key sectors for decarbonisation (mitigation). LÄS MER
22. Analysis of Electricity Usage Time Series with K-means Clustering
Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/ElektricitetsläraSammanfattning : As the amount of collected and analysed data for electricity usage from buildings is increasing it becomes an important component in energy efficiency efforts. A huge problem when developing algorithms fordetection of anomalous electricity usage series is the lack of data setswith annotated normal usage series for training and evaluation purposes. LÄS MER
23. Comparison of initialization methods of K-means clustering for small data
Kandidat-uppsats, Uppsala universitet/Statistiska institutionenSammanfattning : Clustering of observations into groups arises as a fundamental challenge both in academia and industry. Many clustering algorithms exist, and the most widely used clustering algorithm, the K-means, notably suffers from sensitivity to initial allocation of cluster centers. LÄS MER
24. Avatar Playing Style : From analysis of football data to recognizable playing styles
Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Avdelningen för systemteknikSammanfattning : Football analytics is a rapid growing area which utilizes conventional data analysis and computational methods on gathered data from football matches. The results emerging out of this can give insights of performance levels when it comes to individual football players, different teams and clubs. LÄS MER
25. Predict Saving Behavior - Artificial Neural Network & Machine Learning
Master-uppsats, Lunds universitet/Nationalekonomiska institutionenSammanfattning : This study aims to predict saving behavior using Artificial Neural Network (ANN), XGBoost, and Support Vector Machine (SVM) algorithms. First, 25 variables were chosen from the original 217 questions asked by the National Financial Capability Well-Being Survey (2018) NFCS, using exploratory data analysis. LÄS MER