Urgench state University Physics and mathematics faculty Speciality: «5111018-Professional education: Informatics and Information technologies» Group and student name: 181-inf Babaev Saidmukhammadjon

Kafеdra mudiri: dots. Madatov X.A.

Urganch davlat univеrsitеti Fizika - matematika fakultеti
“Axborot texnologiyalari” kafеdrasi
“5111018-“Kasb ta’limi:Informatika va axborot texnologiyalari”
bakalavr ta’lim yo‘nalishi
Tasdiqlayman
Fizika-matematika fakultеti dеkani
___________dоts. J.U.Xujamov
“______”_____________ 2022 y.

BITIRUV MALAKAVIY ISH BO‘YICHA TОPSHIRIQ
Talaba Babayev Saidmuxammadjon Saidkamolovich.

1. 
   Ishning mavzusi: “Big data mining. Comparison the performance of classification models on big datasets.” 2021 yil 10 oktyabrda univеrsitеt rеktоrining __________ buyrug‘i bilan tasdiqlangan.
   

2. Ishni tоpshirish muddati: “20” iyun 2022 y.
3. Mavzu bo‘yicha dastlabki ma’lumоtlar bеruvchi adabiyotlar ro‘yхati

1. 
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2. 
   Ali, K., Manganaris, S., Srikant, R. Partial Classification Using Association Rules. In Proceedings of KDD-97, pp. 115-118, U.S.A (1997).
   
3. 
   Baralis, E., Cagliero, L., Garza, P.: A novel pattern-based Bayesian classifier. IEEE Transactions on Knowledge and Data Engineering 25(12), 2780–2795 (2013).
   
4. 
   Bayardo, R. J. Brute-force mining of high-confidence classification rules. In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, pp. 123-126, U.S.A (1997).
   
5. 
   Breiman L.: Random Forests. Machine Learning 45(1), pp. 5-32 (2001).
   
6. 
   Cendrowska J.: PRISM: An algorithm for inducing modular rules. International Journal of Man-Machine Studies 27(4), pp. 349-370 (1987).
   
7. 
   Chen, G., Liu, H., Yu, L., Wei, Q., Zhang, X.: A new approach to classification based on association rule mining. Decision Support Systems 42(2), 674–689 (2006).
   
8. 
   Clark, P., Niblett, T.: The CN2 induction algorithm. Machine Learning, 3(4), 261–283 (1989).
   
9. 
   Cohen, W., W.: Fast Effective Rule Induction. In: ICML'95 Proceedings of the Twelfth International Conference on Machine Learning, pp. 115-123, Tahoe City, California (1995).
   
10. 
    Dua, D., Graff, C.: UCI Machine Learning Repository, Irvine, CA: University of California (2019).
    
11. 
    Frank, E., Witten, I.: Generating Accurate Rule Sets Without Global Optimization. In: Fifteenth International Conference on Machine Learning, pp. 144-151. USA (1998).
    
12. 
    Holte, R.: Very simple classification rules perform well on most commonly used datasets. Machine Learning 11(1), pp. 63-91 (1993).
    
13. 
    Kohavi, R.: The Power of Decision Tables. In: 8th European Conference on Machine Learning, pp. 174-189, Heraclion, Crete, Greece (1995).
    
14. 
    Lent, B., Swami, A., Widom, J.: Clustering association rules. In: ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering, pp. 220-231. England (1997).
    
15. 
    Li, W., Han, J., Pei, J.: CMAR: accurate and efficient classification based on multiple class-association rules. in Proceedings of the 1st IEEE International Conference on Data Mining (ICDM ’01), pp. 369–376, San Jose, California, USA (2001).
    
16. 
    Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. in Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining (KDD ’98), pp. 80–86, New York, USA (1998).
    
17. 
    Quinlan, J.: C4.5: Programs for Machine Learning, Machine Learning 16(3), 235-240 (1993).
    
18. 
    Xiaoxin, Y., Jiawei, H. CPAR: Classification based on Predictive Association Rules. Proceedings of the SIAM International Conference on Data Mining, pp. 331-335, San Francisco, U.S.A (2003).
    
19. 
    Zhang, M., Zhou Z.: A k-nearest neighbor based algorithm for multi-label classification. In: Proceedings of the 1st IEEE International Conference on Granular Computing (GrC’05), vol. 2, pp. 718–721, Beijing, China (2005).