Title :
Top-N Frequent Itemsets Mining Algorithm Based on Greedy Method
Author :
Hu Ming ; Cui Huan ; Wang Hong-mei ; Zhang Xin-jing
Author_Institution :
Dept. of Comput. Sci. & Eng., Changchun Univ. of Technol., Changchun, China
Abstract :
Top-N frequent item sets mining gets the interesting results by appointing the quantity of the most frequent item sets. It is an important data mining application. This article proposed a Top-N frequent item sets mining algorithm based on greedy method. The obtained Top-N item sets are stored in a static doubly linked list. Join two frequent item sets which have the highest support in the not joined Top-N item sets. The frequent item sets have the higher support that can be generated early. We can adjust the border support quickly. According to the analysis and experiments, this new algorithm demonstrates better performance than Apriori and NApriori on the time and space performance.
Keywords :
data mining; greedy algorithms; Top-N frequent itemsets mining algorithm; data mining; greedy method; static doubly linked list; Algorithm design and analysis; Arrays; Data mining; Greedy algorithms; Itemsets; Time complexity; border support; greedy algorithm; join; most frequent itemsets; static doubly linked list;
Conference_Titel :
Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2014 International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4799-6235-8
DOI :
10.1109/CyberC.2014.47
