DocumentCode
2480418
Title
Efficiently Mining the Recent Frequent Patterns over Online Data Streams
Author
Chen, Hui
Author_Institution
Sch. of Software & Commun. Eng., Jiangxi Univ. of Finance & Econ., Nanchang, China
fYear
2010
fDate
22-23 May 2010
Firstpage
1
Lastpage
4
Abstract
In order to mine the frequent patterns in the recent time windows of the data stream than the historic, a new method was proposed to mine the recent frequent patterns in the sliding window of an online data stream. It calculated the approximate frequencies of patterns in the sliding window with a conservative strategy. Also, it built a Recent Frequent Pattern tree(RFP-tree for short) to incrementally capture the patterns in the sliding window and mine them by scanning the stream only once. Extensive experiments show that the proposed method is more efficient and scalable than other analogous algorithms.
Keywords
data mining; pattern classification; frequent patterns mining; online data streams sliding window; recent frequent pattern tree; Data engineering; Data mining; Data structures; Design methodology; Finance; Frequency; History; Itemsets; Monitoring; Tree data structures;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Systems and Applications (ISA), 2010 2nd International Workshop on
Conference_Location
Wuhan
Print_ISBN
978-1-4244-5872-1
Electronic_ISBN
978-1-4244-5874-5
Type
conf
DOI
10.1109/IWISA.2010.5473377
Filename
5473377
Link To Document