Search for dissertations about: "clustering analysis"
Showing result 1 - 5 of 301 swedish dissertations containing the words clustering analysis.
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1. Data Stream Mining and Analysis : Clustering Evolving Data
Abstract : Streaming data is becoming more prevalent in our society every day. With the increasing use of technologies such as the Internet of Things (IoT) and 5G networks, the number of possible data sources steadily increases. Therefore, there is a need to develop algorithms that can handle the massive amount of data we now generate. READ MORE
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2. Assessment of Telematic Systems for Road Freight Transport
Abstract : The focus of this thesis is the assessment of telematic systems for road freight transport from a planning perspective. The aim is to support strategic decisions related to architectural choices for such systems, with the possibility to achieve synergies by supporting multiple telematic services. READ MORE
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3. Bayesian Cluster Analysis : Some Extensions to Non-standard Situations
Abstract : The Bayesian approach to cluster analysis is presented. We assume that all data stem from a finite mixture model, where each component corresponds to one cluster and is given by a multivariate normal distribution with unknown mean and variance. READ MORE
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4. Clustering Techniques for Mining and Analysis of Evolving Data
Abstract : The amount of data generated is on rise due to increased demand for fields like IoT, smart monitoring applications, etc. Data generated through such systems have many distinct characteristics like continuous data generation, evolutionary, multi-source nature, and heterogeneity. READ MORE
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5. Resource-Aware and Personalized Federated Learning via Clustering Analysis
Abstract : Today’s advancement in Artificial Intelligence (AI) enables training Machine Learning (ML) models on the daily-produced data by connected edge devices. To make the most of the data stored on the device, conventional ML approaches require gathering all individual data sets and transferring them to a central location to train a common model. READ MORE