Data intensive peer-to-peer (P2P) networks are becoming increasingly popular in applications like social networking, file sharing networks, etc. Data mining in such P2P environments is the new generation of advanced P2P applications. Unfortunately, most of the existing data mining algorithms do not fit well in such environments since they require data that can be accessed in its entirety. It also is not easy due to the requirements of online transactional data streams. In this paper, we have developed a local algorithm for tracing frequent item sets over a P2P network. The performance of the proposed algorithm is comparatively tested and analyzed through a series of experiments.
Publié le : 2015-10-19
Classification:  Data stream mining, frequent item set mining, any time algorithm, distributed data mining, P2P data mining
@article{cai3210,
     author = {Zahra Farzanyar; Department of Computer Engineering, Iran University Science and Technology (IUST), Tehran and Mohammadreza Kangavari; Department of Computer Engineering, Iran University Science and Technology (IUST), Tehran},
     title = {Distributed Frequent Item Sets Mining over P2P Networks},
     journal = {Computing and Informatics},
     volume = {33},
     number = {3},
     year = {2015},
     language = {en},
     url = {http://dml.mathdoc.fr/item/cai3210}
}
Zahra Farzanyar; Department of Computer Engineering, Iran University Science and Technology (IUST), Tehran; Mohammadreza Kangavari; Department of Computer Engineering, Iran University Science and Technology (IUST), Tehran. Distributed Frequent Item Sets Mining over P2P Networks. Computing and Informatics, Tome 33 (2015) no. 3, . http://gdmltest.u-ga.fr/item/cai3210/