Mining Frequent Itemsets in Data Streams Based on Genetic Algorithm

被引：0

作者：

Han, Chong ^{[1
]}

Sun, Lijuan ^{[1
,2
,3
]}

Guo, Jian ^{[1
,2
,3
]}

Chen, Xiaodong ^{[1
]}

机构：

[1] Nanjing Univ Posts & Telecommun, Coll Comp, Nanjing 210003, Jiangsu, Peoples R China

[2] Jiangsu High Technol Res Key Lab Wireless Sensor, Nanjing 210003, Jiangsu, Peoples R China

[3] Minist Educ Jiangsu Prov, Key Lab Broadband Wireless Commun & Sensor Networ, Nanjing 210003, Jiangsu, Peoples R China

来源：

2013 15TH IEEE INTERNATIONAL CONFERENCE ON COMMUNICATION TECHNOLOGY (ICCT) | 2013年

关键词：

Big data; data streams; genetic algorithm; frequent itemsets;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Stream data is a very common data type in big data and in many data streams applications, users tend to pay more attention to the mode information of the data streams. So mining frequent patterns in data streams is a significative work. Meanwhile, finding frequent itemests in a data set with predefined fixed support threshold could be seen as an optimization problem. In this paper, the problem of frequent itemsets mining is derived as a non-linear optimization problem, then genetic algorithm is adopted to solve it. Through the formal and bitmap representation of frequent itemsets, the non-linear optimization problem is transformed to 0-1 programming. A set of experimental results show that unlike typical Apriori algorithm, the complexity of time and memory space grows exponentially as the support decrease, our proposed algorithm has a high time and space efficiency even with a very low support.

引用

页码：748 / 753

页数：6

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