Mining Frequent Itemsets over Uncertain Database using Matrix
Deepak Kumar Sharma1, Samar Wazir2, Md. Tabrez Nafis3, Amit Kumar4
1Deepak Kumar Sharma*, (Pursuing M. Tech) Computer Science & Engineering, Jamia Hamdard University, New Delhi, India.
2Dr. Samar Wazir, Computer Science & Engineering, Jamia Hamdard University, New Delhi, India.
3Md. Tabrez Nafis, Computer Science & Engineering, Jamia Hamdard University, New Delhi, India.
4Amit Kumar, (Pursuing M. Tech) Computer Science & Engineering, Jamia Hamdard University, New Delhi, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 25, 2020. | Manuscript published on April 10, 2020. | PP: 2048-2052 | Volume-9 Issue-6, April 2020. | Retrieval Number: F3824049620/2020©BEIESP | DOI: 10.35940/ijitee.F3824.049620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In the area of data mining for finding frequent itemset from huge database, there exist a lot of algorithms, out of all Apriori algorithm is the base of all algorithms. In Uapriori algorithm each items existential probability is examined with a given support count, if it is greater or equal then these items are known as frequent items, otherwise these are known as infrequent itemsets. In this paper matrix technology has been introduced over Uapriori algorithm which reduces execution time and computational complexity for finding frequent itemset from uncertain transactional database. In the modern era, volume of data is increasing exponentially and highly optimized algorithm is needed for processing such a large amount of data in less time. The proposed algorithm can be used in the field of data mining for retrieving frequent itemset from a large volume of database by taking very less computation complexity. .
Keywords: Certain Transactional Database, Uncertain Transaction Dataset, Existential Probability, Matrix, Data Mining, Machine Learning
Scope of the Article: Machine Learning