KATTA MA’LUMOTLAR TO‘PLAMLARIDA ASSOTSIATIV QOIDALARNI QIDIRISH ALGORITMLARI.
Keywords:
Assotsiativ qoidalarni qidirish, katta ma’lumotlar tahlili, Machine Learning algoritmlari, Apriori algoritmi, FP-Growth algoritmi, Eclat algoritmi, qo‘llab-quvvatlash (Support), ishonchlilik (Confidence), lift ko‘rsatkichi, ma’lumotlarga asoslangan qaror qabul qilish, savdo va marketing tahlili, internet xavfsizligi, ma’lumotlar qazib olish, assotsiativ tahlil, yashirin munosabatlarni aniqlash, ma’lumotlarni segmentatsiya qilish, patronlarni tanib olish, sotib olish xatti-harakatlarini prognoz qilish, BI (Business Intelligence).Abstract
Ushbu maqola katta ma’lumotlar to‘plamlarida yashirin assotsiativ qoidalarni qidirish va aniqlash uchun ishlatiladigan algoritmlar haqida keng qamrovli tahlilni taqdim etadi. Ma’lumotlarda mavjud bo‘lgan bog‘liqlik va munosabatlarni kashf qilish, ayniqsa, tijorat, marketing, biotibbiyot, va internet xavfsizligi kabi ko‘plab sohalarda katta ahamiyatga ega. Assotsiativ qoidalarni qidirish algoritmlari, jumladan, Apriori, FP-Growth va Eclat kabi algoritmlar, katta hajmdagi ma’lumotlarda tez-tez sodir bo‘ladigan hodisalar va ular o‘rtasidagi aloqalarni aniqlash uchun qo‘llaniladi. Ushbu maqola har bir algoritmning ishlash prinsipi, ularning afzallik va cheklovlari, shuningdek, ularning ko‘p sohalarda qo‘llanilishdagi amaliy ahamiyatini ochib beradi. Maqolada katta ma’lumotlar to‘plamlarida yuqori samaradorlikka erishish uchun ushbu algoritmlardan qanday foydalanish kerakligi ham yoritiladi. Assotsiativ qoidalar orqali ma’lumotlardagi muhim munosabatlarni aniqlash va undan kelib chiqadigan qarorlarni qabul qilish jarayonlariga yordam beruvchi usullar haqida batafsil ma’lumot keltiriladi.
References
1. Agrawal, R., & Shafer, J. (1996). Parallel Mining of Association Rules. IEEE Transactions on Knowledge and Data Engineering, 8(6), 962-969.
2. Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), 487-499.
3. Cheung, D., & Ng, S. (2000). A Fast and Memory Efficient Algorithm for Mining Association Rules. IEEE Transactions on Knowledge and Data Engineering, 12(6), 886-889.
4. Han, J., & Kamber, M. (2006). Data Mining: Concepts and Techniques (2nd ed.). Morgan Kaufmann Publishers.
5. Jhanwar, S., & Choudhary, A. (2015). A Comparative Study of Association Rule Mining Algorithms: Apriori, Eclat, FP-growth. International Journal of Computer Applications, 112(9), 1-6.
6. Liu, B., Hsu, W., & Ma, Y. (1998). Mining Association Rules with Multiple Minimum Supports. Proceedings of the 1998 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 337-341.
7. Srivastava, J., & Srikant, R. (1999). Mining Generalized Association Rules. Proceedings of the 21st International Conference on Very Large Data Bases (VLDB), 407-419.
8. Tan, P.-N., Steinbach, M., & Kumar, V. (2006). Introduction to Data Mining. Addison Wesley.
9. Zaki, M. J., & Hsiao, C.-J. (2002). CHARM: An Efficient Algorithm for Closed Itemset Mining. Proceedings of the 2002 ACM SIGMOD International Conference on Management of Data, 505-516.
