Τεχνικές ομαδοποίησης και η χρήση τους σε λογισμικά με εξατομικευμένες υπηρεσίες
Clustering algorithms and their implementation in personalized services
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Keywords
Αλγόριθμοι ομαδοποίησης ; Εξατομικευμένες υπηρεσίες ; Μηχανική μάθηση ; Ανάλυση δεδομένων ; Εφαρμογές ομαδοποίησηςAbstract
The study examines the use of clustering algorithms to enhance personalized services, focusing on their importance for data analysis and improving user experience. Clustering algorithms, such as K-means, hierarchical clustering and DBSCAN, are analyzed in detail to understand their efficiency and their application in different domains. The study includes data collection and analysis, implementation of the algorithms and evaluation of the results in real-life personalized service settings.
By analyzing the results, the study identifies which algorithms prove to be most effective in specific scenarios and how their implementation affects the quality of personalized services. Comparisons between different algorithms highlight their strengths and weaknesses, providing valuable insights for improving their application in different business and technological areas.
In the discussion, the main findings of the study and their implications for theory and practice are highlighted. The limitations of the research are discussed and directions for future research are suggested to further understand and improve the use of clustering algorithms. The resulting recommendations provide useful guidance for practitioners and researchers involved in data analysis and the development of personalized services.
The scientific area of study is "Data Analysis and Machine Learning", with a special focus on the use of clustering algorithms for the development of personalized services.