A partition enhanced mining algorithm for distributed association rule mining systems
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Date
2015
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Elsevier.
Abstract
The extraction of patterns and rules from large distributed databases through existing
Distributed Association Rule Mining (DARM) systems is still faced with enormous challenges such
as high response times, high communication costs and inability to adapt to the constantly changing
databases. In this work, a Partition Enhanced Mining Algorithm (PEMA) is presented to address
these problems. In PEMA, the Association Rule Mining Coordinating Agent receives a request and
decides the appropriate data sites, partitioning strategy and mining agents to use. The mining process
is divided into two stages. In the first stage, the data agents horizontally segment the databases
with small average transaction length into relatively smaller partitions based on the number of
available sites and the available memory. On the other hand, databases with relatively large average
transaction length were vertically partitioned. After this, Mobile Agent-Based Association Rule
Mining-Agents, which are the mining agents, carry out the discovery of the local frequent itemsets.
At the second stage, the local frequent itemsets were incrementally integrated by the from one data
site to another to get the global frequent itemsets. This reduced the response time and communication
cost in the system. Results from experiments conducted on real datasets showed that the average
response time of PEMA showed an improvement over existing algorithms. Similarly, PEMA
incurred lower communication costs with average size of messages exchanged lower when compared
with benchmark DARM systems. This result showed that PEMA could be efficiently deployed for
efficient discovery of valuable knowledge in distributed databases.
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Citation
Ogunde A. O., Folorunso O. and Sodiya A. S. (2015). A Partition Enhanced Mining Algorithm for Distributed Association Rule Mining. Egyptian Informatics Journal, Volume 16, Issue 3, Pages 297–307