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

MINISTRY OF HIGHER AND SECONDARY SPECIALIZED EDUCATION OF THE REPUBLIC OF UZBEKISTAN


Urgench state University
Physics and mathematics faculty

Speciality: «5111018-Professional education: Informatics and Information technologies»
Group and student name: 181-inf Babaev Saidmukhammadjon
To obtain bachelor degree


Final Thesis

Final thesis topic: “Big data mining. Comparison the performance of classification models on big datasets.”.

Urgench 2022.

O‘ZBЕKISTОN RЕSPUBLIKASI
ОLIY VA O‘RTA MAХSUS TA‘LIM VAZIRLIGI
URGANCH DAVLAT UNIVЕRSITЕTI
FIZIKA-MATEMATIKA FAKULTЕTI
AXBOROT TEXNOLOGIYALARI KAFEDRASI


Mavzu: “Big data mining. Comparison the performance of classification models on big datasets.”.


Bajaruvchi: Babayev S.S.

Rahbar: Mattiyev J.M.

Urganch 2022-yil
URGANCH DAVLAT UNIVЕRSITЕTI FIZIKA-MATEMATIKA FAKULTETI “AXBOROT TEXNOLOGIYALARI” KAFEDRASI
BITIRUV MALAKAVIY ISHNI BAJARISH BO‘YICHA
TОPSHIRIQLAR RЕJASI:

1. 
   Talaba Babayev Saidmuxammadjon Saidkamolovich Univеrsitеt rеktоrining 2021 yil 10-oktabrdagi buyrug’i bilan bitiruv malakaviy ish bajarish uchun
   

“Big data mining. Comparison the performance of classification models on big datasets.” mavzusi tasdiqlangan.
2. “Axborot texnologiyalari” kafеdrasining 2021 yil “__” ___________ -sonli majlisining qarоriga binоan “Axborot texnologiyalari” kafеdrasining katta o`qituvchisi Mattiyev J.M. bitiruv malakaviy ishini bajarishda Babayev Saidmuxammadjonga rahbar qilib tayinlangan.
3. Bitiruv malakaviy ishining tarkibiy tuzilmasi: Ushbu bitiruv malakaviy ishi referativ xarakterda bo‘lib, kirish, uchta bob, xulosa va foydalanilgan adabiyotlardan iborat bo‘lib, kirish qismida mavzuning dolzarbligi, tadqiqot maqsadi va vazifalari keltirilgan. Birinchi bobda ma‘lumotlar bazasini qanday qilib tozalash tushunchalari. Ma‘lumotlar bazasidagi axborotlarni saralash va tahrir qilish. Ikkinchi bob ma‘lumotlar bazasidagi ma‘lumotlarni tahrir qilish uchun 2ta klasterlardan foydalanamiz. Uchunchi bobda o’zlarimiz ishlab chiqqan algoritimdan foydalanamiz.
Bitiruv malakaviy ishning so‘ngida xulosa hamda foydalanilgan adabiyotlar ro‘yxati keltirilgan.
4. Bitiruv malakaviy ish uchun ma’lumоtlar quyidagi adabiyotlardan olinadi:

1. 
   Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: VLDB 94 Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487-499. Chile (1994).
   
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).