Χρήση τεχνικών ομαδοποίησης στον επιχειρηματικό σχεδιασμό
Application of cluster analysis techniques in business analytics
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Keywords
Ομαδοποίηση ; Επιχειρηματικός σχεδιασμός ; Clustering ; Business analytics ; K-means ; DBSCANAbstract
We are in the Big Data era which exists in many areas, as well as in the field of business planning. At the same time, the strategic framework in a business plan is very critical to its success. In this regard, Machine Learning can contribute to the more efficient design of a business plan.
Through machine learning algorithms we can extract knowledge from data, allowing for a more realistic data analysis. A key pillar of Machine Learning is unsupervised learning, where data clustering algorithms predominate. In cases where we have data without a label (or class), clustering techniques can better capture the structure of this data offering a better business plan design.
In this thesis, we compare traditional clustering algorithms in real world data related to business planning problems. The results showed the superiority of partitioning algorithms and highlight the limitations that arise when we apply the wrong algorithm to a case under study.
Postgraduate Studies Programme
Εφαρμοσμένη ΣτατιστικήDepartment
Σχολή Χρηματοοικονομικής και Στατιστικής. Τμήμα Στατιστικής και Ασφαλιστικής ΕπιστήμηςNumber of pages
82Language
GreekCollections
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Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα
Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα