TY - GEN
T1 - MFI-transSW+
T2 - 17th International Conference on E-Commerce and Web Technologies, EC-Web 2016
AU - De Amorim, Franklin A.
AU - Nunes, Bernardo Pereira
AU - Lopes, Giseli Rabello
AU - Casanova, Marco A.
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Data mining
KW - Datastream
KW - Frequent itemsets
UR - http://www.scopus.com/inward/record.url?scp=85013277475&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-53676-7_7
DO - 10.1007/978-3-319-53676-7_7
M3 - Conference contribution
SN - 9783319536750
T3 - Lecture Notes in Business Information Processing
SP - 87
EP - 99
BT - E-Commerce and Web Technologies - 17th International Conference, EC-Web 2016, Revised Selected Papers
A2 - Bridge, Derek
A2 - Stuckenschmidt, Heiner
PB - Springer Verlag
Y2 - 5 September 2016 through 8 September 2016
ER -