Sökning: "Density-Based Clustering"

Visar resultat 1 - 5 av 29 uppsatser innehållade orden Density-Based Clustering.

  1. 1. Exploring Advanced Clustering Techniques for Business Descriptions : A Comparative Study and Analysis of DBSCAN, K-Means, and Hierarchical Clustering

    Uppsats för yrkesexamina på avancerad nivå, Mittuniversitetet/Institutionen för data- och elektroteknik (2023-)

    Författare :Wisam Orabi Alkhen; [2023]
    Nyckelord :Machine learning; Business descriptions; Search scope reduction; Relevant business terminology; Data analysis.;

    Sammanfattning : In this study, we introduce several approaches to analyze large volumes of business descriptions by applying machine learning clustering and classification algorithms. The goal is to efficiently classify these descriptions, reducing the search scope and allowing for better business insights and decision-making processes. LÄS MER

  3. 2. Decoding communication of non-human species - Unsupervised machine learning to infer syntactical and temporal patterns in fruit-bats vocalizations.

    Master-uppsats, Stockholms universitet/Institutionen för data- och systemvetenskap

    Författare :Luigi Assom; [2023]
    Nyckelord :animal decision making; unsupervised machine learning; UMAP; autoencoders; classifiers; bioacoustics; combinatory syntax; animal communication;

    Sammanfattning : Decoding non-human species communication offers a unique chance to explore alternative intelligence forms using machine learning. This master thesis focuses on discreteness and grammar, two of five linguistic areas machine learning can support, and tackles inferring syntax and temporal structures from bioacoustics data annotated with animal behavior. LÄS MER

  4. 3. Automatic Interpretation of Ion Beam Measurements of Walls in Fusion Machines

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

    Författare :Daniel Lundberg; Ludvig Johansson; [2023]
    Nyckelord :;

    Sammanfattning : The purpose of this study is to investigate whether it is possible to automatically interpret theresults of Time-of-flight Elastic Recoil Detection Analysis (ToF-ERDA). And if so, find out if the automaticinterpretation is quicker and/or more accurate than the current approach that consists of manualanalysis. LÄS MER

  5. 4. Intelligence Extraction Using Machine Learning for Threat Identification Purposes : An Overview

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

    Författare :Jonatan Lindgren; [2022]
    Nyckelord :Machine learning; Radar threat identification; Clustering; Performance metrics for unsupervised learning; Feature scaling; Electronic warfare; Maskininlärning; Identifikation av radarhot; Klustring; Prestandamått för oövervakad inlärning; Skalning av dataparametrar; Elektronisk krigsföring;

    Sammanfattning : Radar is an invaluable tool for detecting and assessing threats on land, on the seas and in the air. To properly evaluate threats, radar operators construct threat libraries where the signal characteristics of emitters are stored and mapped to specific types of platforms. LÄS MER

  7. 5. Offline Direction Clustering of Overlapping Radar Pulses from Homogeneous Emitters

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

    Författare :Sofia Bedoire; [2022]
    Nyckelord :Clustering; DBSCAN; Deinterleaving; Machine Learning; Radar; Klustring; DBSCAN; Pulssortering; Maskininlärning; Radar;

    Sammanfattning : Within the defence industry, it is essential to be aware of threats in the environment. A potential threat can be detected by identifying certain types of emitters in the surroundings that are typically used in the enemies’ systems. LÄS MER