Paper Info
Title
MFI-TransSW plus : Efficiently Mining Frequent Itemsets in Clickstreams
Abstract
Data stream mining is the process of extracting knowledge from massive real-time sequence of data items arriving at a very high data rate. It has several practical applications, such as user behavior analysis, software testing and market research. However, the large amount of data generated may offer challenges to process and analyze data at nearly real time. In this paper, we first present the MFI-TransSW+ algorithm, an optimized version of MFI-TransSW algorithm that efficiently processes clickstreams, that is, data streams where the data items are the pages of a Web site. Then, we outline the implementation of a news articles recommender system, called ClickRec, to demonstrate the efficiency and applicability of the proposed algorithm. Finally, we describe experiments, conducted with real world data, which show that MFI-TransSW+ outperforms the original algorithm, being up to two orders of magnitude faster when processing clickstreams.
Year
DOI
Venue
2016
10.1007/978-3-319-53676-7_7
Lecture Notes in Business Information Processing
Keywords
Field
DocType
Datastream,Frequent itemsets,Data mining
Recommender system,Data mining,Data stream mining,Computer science,Data rate,Marketing,Market research,Web site,Software testing
Conference
Volume
ISSN
Citations 
278
1865-1348
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Franklin A. de Amorim100.34
Bernardo Pereira Nunes218530.96
Giseli Rabello Lopes310716.44
Marco A. Casanova41007979.09