ASSOTSIATIV QOIDALAR VA BOZOR SAVATLARINING TAHLILI

ASSOTSIATIV QOIDALAR VA BOZOR SAVATLARINING TAHLILI

Авторы

  • Alisher Qobilov TDIU
  • Muzaffar Abdulaxatov TDIU
  • Sherzod Rajabov TDIU
  • Sanjar Zokirov TDIU

Ключевые слова:

Assotsiativ qoidalar, Apriora, Bozor savatchalari, Genetik algoritmlar, Cell protsessorlar, Intel protsessorlari

Аннотация

Ushbu maqolada xizmat ko‘rsatish va tijorat yo‘nalishidagi zamonaviy tashkilotlar plastik kartochkalar va nazorat qiluvchi kompyuter tizimlari orqali qilingan har bir buyurtma to‘g‘risida aniq ma’lumotlarni yig‘ib, ma’lumotlarni yozish va saqlash texnologiyasi yordamida iste’molchilar tomonidan qilingan xarid, buyurtma va xizmatlar haqida katta xajmdagi ma’lumotlarni to‘planish, menejment va marketing sohasidagi mutaxassislar tomonidan xaridorlarning xatti xarakatlarida qonuniyatlarni aniqlash, ularning iste’molchilik bilimlari, xatti xarakatlari tashkilotning marketing va mahsulotlar siyosatini boshqarish va tashkilotning daromadi va raqobatbardoshligini oshirishda, zamonaviy axborot texnologiyalari sohasida ma’lumotlarni intellektual tahlil qilish yordamida yig‘ilgan statistik ma’lumotlarni tahlil qilish vositalar tahlili masalalari yoritib berilgan.

Биографии авторов

Alisher Qobilov, TDIU

“Raqamli iqtisodiyot va axborot texnologiyalari” kafedrasi dotsenti

Muzaffar Abdulaxatov , TDIU

“Raqamli iqtisodiyot va axborot texnologiyalari” kafedrasi katta o’qituvchisi

Sherzod Rajabov, TDIU

“Raqamli iqtisodiyot va axborot texnologiyalari” kafedrasi katta o’qituvchisi

Sanjar Zokirov, TDIU

“Raqamli iqtisodiyot va axborot texnologiyalari” kafedrasi assistenti

Библиографические ссылки

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Xiong, X. He, C. Ding, Y. Zhang, V. Kumar, and S. R. Holbrook. Identification of Functional Modules in Protein Complexes via Hyperclique Pattern Discovery. In Proc. of the Pacific Symposium on Biocomputing, (PSB 2005), Maui, January 2010.

H. Xiong, S. Shekhar, P. N. Tan, and V. Kumar. Exploiting a Support-based Upper Bound of Pearson’s Correlation Coefficient for Efficiently Identifying Strongly Corre¬lated Pairs. In Proc. of the 10th Intl. Conf. on Knowledge Discovery and Data Mining, pages 334-343, Seattle, WA, August 2010.

H. Xiong, M. Steinbach, P. N. Tan, and V. Kumar. HICAP: Hierarchial Clustering with Pattern Preservation. In Proc. of the SIAM Intl. Conf. on Data Mining, pages 279-290, Orlando, FL, April 2011.

H. Xiong, P. N. Tan, and V. Kumar. Mining Strong Affinity Association Patterns in Data Sets with Skewed Support Distribution. In Proc. of the 2003 IEEE Intl. Conf. on Data Mining, pages 387-394, Melbourne, FL, 2010.

X. Yan and J. Han. gSpan: Graph-based Substructure Pattern Mining. In Proc. of the 2002 IEEE Inti Conf. on Data Mining, pages 721-724, Maebashi City, Japan, December 2012.

C. Yang, U. M. Fayyad, and P. S. Bradley. Efficient discovery of error-tolerant frequent itemsets in high dimensions. In Proc. of the 7th Intl. Conf. on Knowledge Discovery and Data Mining, pages 194-203, San Francisco, CA, August 2011.

Загрузки

Опубликован

2023-10-30

Как цитировать

Qobilov, A., Abdulaxatov , M., Rajabov, S., & Zokirov, S. (2023). ASSOTSIATIV QOIDALAR VA BOZOR SAVATLARINING TAHLILI. Raqamli Iqtisodiyot Va Axborot Texnologiyalari, 3(3), 115–120. Retrieved from https://dgeconomy.tsue.uz/index.php/dgeco/article/view/165

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Раздел

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