Sökning: "DBSCAN"

Visar resultat 26 - 30 av 65 uppsatser innehållade ordet DBSCAN.

  1. 26. Anomaly Detection in Log Files Using Machine Learning

    M1-uppsats, Luleå tekniska universitet/Institutionen för system- och rymdteknik

    Författare :Philip Björnerud; [2021]
    Nyckelord :Anomaly detection; Log files; Data representation; Machine learning; Clustering;

    Sammanfattning : Logs generated by the applications, devices, and servers contain information that can be used to determine the health of the system. Manual inspection of logs is important, for example during upgrades, to determine whether the upgrade and data migration were successful. LÄS MER

  3. 27. Anomaly detection in Network data with unsupervised learning methods

    Kandidat-uppsats, Mälardalens högskola/Akademin för innovation, design och teknik

    Författare :George Sarossy; [2021]
    Nyckelord :;

    Sammanfattning : Anomaly detection has become a crucial part of the protection of information and integrity. Due to the increase of cyber threats the demand for anomaly detection has grown for companies. Anomaly detection on time series data aims to detect unexpected behavior on the system. LÄS MER

  4. 28. Clustering and Summarization of Chat Dialogues : To understand a company’s customer base

    Master-uppsats, Linköpings universitet/Artificiell intelligens och integrerade datorsystem

    Författare :Oskar Hidén; David Björelind; [2021]
    Nyckelord :Machine Learning; NLP; Text Representations; Clustering; Extractive summarization; TFIDF; S-BERT; FastText; K-means; DBSCAN; HDBSCAN; LSA; TextRank; Word Mover s Distance WMD ;

    Sammanfattning : The Customer Success department at Visma handles about 200 000 customer chats each year, the chat dialogues are stored and contain both questions and answers. In order to get an idea of what customers ask about, the Customer Success department has to read a random sample of the chat dialogues manually. LÄS MER

  5. 29. Unsupervised Anomaly Detection on Time Series Data: An Implementation on Electricity Consumption Series

    Master-uppsats, KTH/Matematisk statistik

    Författare :Amelia Lindroth Henriksson; [2021]
    Nyckelord :Unsupervised learning; machine learning; anomaly detection; time series; electricity consumption; synthetic anomalies; DBSCAN; LOF; iForest; OC-SVM; Oövervakad inlärning; maskininlärning; anomalidetektion; tidsserier; elförbrukning; syntetiska anomalier; DBSCAN; LOF; iForest; OC-SVM;

    Sammanfattning : Digitization of the energy industry, introduction of smart grids and increasing regulation of electricity consumption metering have resulted in vast amounts of electricity data. This data presents a unique opportunity to understand the electricity usage and to make it more efficient, reducing electricity consumption and carbon emissions. LÄS MER

  7. 30. Automated error matching system using machine learning and data clustering : Evaluating unsupervised learning methods for categorizing error types, capturing bugs, and detecting outliers.

    Master-uppsats, Linköpings universitet/Programvara och system

    Författare :Jonatan Bjurenfalk; August Johnson; [2021]
    Nyckelord :Unsupervised learning; machine learning; clustering; DBSCAN; HDBSCAN; X-Means; outlier detection; error log clustering;

    Sammanfattning : For large and complex software systems, it is a time-consuming process to manually inspect error logs produced from the test suites of such systems. Whether it is for identifyingabnormal faults, or finding bugs; it is a process that limits development progress, and requires experience. LÄS MER