P2P-FISM: Mining (recently) frequent item sets from distributed data streams over P2P network

Authors:
Zahra Farzanyar;Mohammadreza Kangavari;Nick Cercone
Affiliations:
Department of Computer Engineering, Iran University Science & Technology, Tehran, Iran;Department of Computer Engineering, Iran University Science & Technology, Tehran, Iran;Department of Computer Science and Engineering, York University, Toronto, Canada
Venue:
Information Processing Letters
Year:
2013

Citing 4
Cited 0

Fast Algorithms for Mining Association Rules in Large Databases

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A communication efficient probabilistic algorithm for mining frequent itemsets from a peer-to-peer network

Statistical Analysis and Data Mining
Association rule mining in peer-to-peer systems

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Max-FISM: Mining (recently) maximal frequent itemsets over data streams using the sliding window model

Computers & Mathematics with Applications

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Abstract

Data intensive large-scale distributed systems like peer-to-peer (P2P) networks are finding large number of applications for social networking, file sharing networks, etc. Global data mining in such P2P environments may be very costly due to the high scale and the asynchronous nature of the P2P networks. The cost further increases in the distributed data stream scenario where peers receive continuous sequence of transactions rapidly. In this paper, we develop an efficient local algorithm, P2P-FISM, for discovering of the network-wide recent frequent itemsets. The algorithm works in a completely asynchronous manner, imposes low communication overhead, a necessity for scalability, transparently tolerates network topology changes, and quickly adapts to changes in the data stream. The paper demonstrates experimental results to corroborate the theoretical claims.