Sökning: "Gaussian Mixture Models Cluster Analysis"

Visar resultat 1 - 5 av 6 uppsatser innehållade orden Gaussian Mixture Models Cluster Analysis.

1. 1. Exploring Greenhouse Gas Emissions and socio-economic factors for climate change mitigation: A worldwide clustering analysis

   Magister-uppsats, Lunds universitet/Nationalekonomiska institutionen; Lunds universitet/Statistiska institutionen

   Författare :Anna Pasini; [2023]
   Nyckelord :GHG emissions; Climate Change; Panel Data; Gaussian Mixture Models Cluster Analysis; PCA; Business and Economics;

   Sammanfattning : In response to the pressing need to address climate change and reduce global greenhouse gas (GHG) emissions, this study implemented Gaussian Mixture Models Clustering to detect the levels of GHG emissions and related socio-economic factors in 174 countries. To handle the panel data, Principal Component Analysis was conducted to achieve dimension reduction. LÄS MER

3. 2. Unsupervised Anomaly Detection and Root Cause Analysis in HFC Networks : A Clustering Approach

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Povel Forsare Källman; [2021]
   Nyckelord :Anomaly Detection; Root Cause Analysis; Cluster Analysis; k- means; Self- Organizing Map; Gaussian Mixture Model; Dimensionality Reduction; Principal Component Analysis; Hybrid Fiber- Coaxial Network.; Anomalidetektering; Rotfelsanalys; Klusteranalys; k- means; Self- Organizing Map; Gaussian Mixture Model; Dimensionsreducering; Principal Component Analysis; Hybrid Fiber Coax- nät.;

   Sammanfattning : Following the significant transition from the traditional production industry to an informationbased economy, the telecommunications industry was faced with an explosion of innovation, resulting in a continuous change in user behaviour. The industry has made efforts to adapt to a more datadriven future, which has given rise to larger and more complex systems. LÄS MER

4. 3. Tick data clustering analysis establishing support and resistance levels of the EUR-USD exchange market

   Master-uppsats, Lunds universitet/Matematisk statistik

   Författare :Karl Tengelin; [2020]
   Nyckelord :Tick data; Support-and resistance levels; Clustering methods; Gaussian mixture model; Kmeans; EUR-USD exchange rates; Clustering performance metrics; Market activity; Mathematics and Statistics;

   Sammanfattning : Our aim is to use clustering algorithms in order to compute support and resistance levels within an intra-day trading setting. To achieve this we use a tick data set from the EUR-USD exchange market during 2019 as a measure of market activity. LÄS MER

5. 4. Hierarchical Clustering of Time Series using Gaussian Mixture Models and Variational Autoencoders

   Master-uppsats, Lunds universitet/Matematisk statistik

   Författare :Per Wilhelmsson; [2019]
   Nyckelord :Clustering; Deep Learning; Machine Learning; Time Series; Variational Autoencoders; Gaussian Mixture Models; Mathematics and Statistics;

   Sammanfattning : This thesis proposes a hierarchical clustering algorithm for time series, comprised of a variational autoencoder to compress the series and a Gaussian mixture model to merge them into an appropriate cluster hierarchy. This approach is motivated by the autoencoders good results in dimensionality reduction tasks and by the likelihood framework given by the Gaussian mixture model. LÄS MER

7. 5. Customer segmentation of retail chain customers using cluster analysis

   Master-uppsats, KTH/Matematisk statistik

   Författare :Sebastian Bergström; [2019]
   Nyckelord :Cluster analysis; customer segmentation; tEIGEN; MCLUST; K-means; NMF; Silhouette; Davies-Bouldin; big spenders; statistics; applied mathematics; unsupervised learning; Klusteranalys; kundsegmentering; tEIGEN; MCLUST; K-means; NMF; Silhouette; Davies-Bouldin; storkonsumenter; statistik; tillämpad matematik;

   Sammanfattning : In this thesis, cluster analysis was applied to data comprising of customer spending habits at a retail chain in order to perform customer segmentation. The method used was a two-step cluster procedure in which the first step consisted of feature engineering, a square root transformation of the data in order to handle big spenders in the data set and finally principal component analysis in order to reduce the dimensionality of the data set. LÄS MER