TY - GEN
T1 - Performance analysis of apriori and FP-growth algorithms on online-store data
AU - Sharma, Bhavya
AU - Rao, Anuradha
N1 - Publisher Copyright:
© Grenze Scientific Society, 2021.
PY - 2021
Y1 - 2021
N2 - Mining frequent item sets from transaction datasets is always being one of the most important problems of data mining. Apriori is the most popular and simplest algorithm for frequent itemset mining. Transaction data is a set of recording data resulting in connections with sales-purchase activities at a particular online store. In recent years transaction data has been used in research for discovering new information. One of the possible attempts is to design an application that can be used to analyze the existing transaction data. This paper focuses on the performance comparison of Apriori and FP-growth algorithms with respect to the execution time.
AB - Mining frequent item sets from transaction datasets is always being one of the most important problems of data mining. Apriori is the most popular and simplest algorithm for frequent itemset mining. Transaction data is a set of recording data resulting in connections with sales-purchase activities at a particular online store. In recent years transaction data has been used in research for discovering new information. One of the possible attempts is to design an application that can be used to analyze the existing transaction data. This paper focuses on the performance comparison of Apriori and FP-growth algorithms with respect to the execution time.
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M3 - Conference contribution
AN - SCOPUS:85117844596
T3 - 12th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2021
SP - 654
EP - 660
BT - 12th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2021
PB - Grenze Scientific Society
T2 - 12th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2021
Y2 - 27 August 2021 through 28 August 2021
ER -