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1	An Evaluation of Clustering and Classification Algorithms in Life-Logging Devices Amlinger, Anton January 2015 (has links) Using life-logging devices and wearables is a growing trend in today’s society. These yield vast amounts of information, data that is not directly overseeable or graspable at a glance due to its size. Gathering a qualitative, comprehensible overview over this quantitative information is essential for life-logging services to serve its purpose. This thesis provides an overview comparison of CLARANS, DBSCAN and SLINK, representing different branches of clustering algorithm types, as tools for activity detection in geo-spatial data sets. These activities are then classified using a simple model with model parameters learned via Bayesian inference, as a demonstration of a different branch of clustering. Results are provided using Silhouettes as evaluation for geo-spatial clustering and a user study for the end classification. The results are promising as an outline for a framework of classification and activity detection, and shed lights on various pitfalls that might be encountered during implementation of such service. Clustering Classification DBSCAN CLARANS SLINK Life-Logging Bayesian Inference
2	An Approach To Cluster And Benchmark Regional Emergency Medical Service Agencies Kondapalli, Swetha 06 August 2020 (has links) No description available. Industrial Engineering Statistics Computer Science Emergency Medical Services Unsupervised Learning Random Forest Feature selection Clustering Benchmarking CLARANS K-means K-medoids Machine Learning Python Precision Recall Silhouette Elbow method

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An Evaluation of Clustering and Classification Algorithms in Life-Logging Devices

An Approach To Cluster And Benchmark Regional Emergency Medical Service Agencies