K-MEANS ALGORITMIGA ASOSLANGAN MIJOZLARNI SEGMENTLASH USULINING TAHLILI
Keywords:
mashinali o‘qitish, klasterlash, K-means, mijozlarni segmentlash, ma’lumotlar tahlili, marketing.Abstract
Ushbu maqolada mashinali o‘qitishning nazoratsiz o‘qitish
usullaridan biri bo‘lgan K-means klasterlash algoritmi yordamida mijozlarni
segmentlash masalasi o‘rganildi. Mijozlarning xarid faoliyati, yillik sarf-xarajatlari va
tashrif chastotasiga asoslangan ma’lumotlar to‘plami tahlil qilindi. Natijalar K-means
algoritmi mijozlarni o‘zaro farqlanuvchi guruhlarga samarali ajratishini ko‘rsatdi.
Olingan segmentlar marketing strategiyalarini optimallashtirish uchun muhim amaliy
ahamiyatga ega.
References
Anderberg, M. R. Cluster Analysis for Applications. Academic Press, 1973.
Arthur, D., Vassilvitskii, S. K-means++: The Advantages of Careful Seeding.
Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete
Algorithms, 2007.
Jain, A. K. Data Clustering: 50 Years Beyond K-means. Pattern Recognition Letters,
2010.
MacQueen, J. Some Methods for Classification and Analysis of Multivariate
Observations. Proceedings of the Fifth Berkeley Symposium on Mathematical
Statistics and Probability, 1967.
Scikit-learn Documentation. Machine Learning in Python. https://scikit-learn.org
Tan, P. N., Steinbach, M., Kumar, V. Introduction to Data Mining. Pearson Education,
2006.
Wu, X., Kumar, V., et al. Top 10 Algorithms in Data Mining. Knowledge and
Information Systems, 2008