Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : Association Rules


Proposed Parallel Association Rules Algorithm

Emad kadhiem Jabbar; Waheed Abd Al-Kadhiem Salman

Engineering and Technology Journal, 2014, Volume 32, Issue 1, Pages 157-168

Data mining is an advanced technique for extracting knowledge from a large amount of data for classification, prediction, estimation, clustering or association rules or any activities, which need decision. Mining for associations rules between items in large transactional distributed databases is a central problem in the field of knowledge discovery. When distributed databases are merged at single machine to mining knowledge it will require a large capacity of storage, long execution time in addition to transferring a huge volume of data over network might take extremely long time and also require an unbearable financial cost. In this paper an algorithm is presented toward saving communication cost over the network, central storage cost requirements, and accelerating required execution time. In this paper a new algorithm is proposed, called Proposed Parallel Association Rules Algorithm (PPARA) which aims to extract association rules from one record only for each site from distributed association rules in parallel instead of extracting association rules from huge quantity of distributed data at several sites in parallel, and that is through collecting the one record of local association rules from each site and storing it, these Local Association Rules turn in to produce global association rules over distributed systems in parallel.

Intrusion Detection and Attack Classifier Based on Three Techniques: A Comparative Study

Adel Sabry Issa; Adnan Mohsin Abdulazeez Brifcani

Engineering and Technology Journal, 2011, Volume 29, Issue 2, Pages 386-412

Different soft-computing based methods have been proposed in recent years
for the development of intrusion detection systems. The purpose of this work is to
development, implement and evaluate an anomaly off-line based intrusion
detection system using three techniques; data mining association rules, decision
trees, and artificial neural network, then comparing among them to decide which
technique is better in its performance for intrusion detection system. Several
methods have been proposed to modify these techniques to improve the
classification process. For association rules, the majority vote classifier was
modified to build a new classifier that can recognize anomalies. With decision
trees, ID3 algorithm was modified to deal not only with discreet values, but also
to deal with numerical values. For neural networks, a back-propagation algorithm
has been used as the learning algorithm with different number of input patterns
(118, 51, and 41) to introduce the important knowledge about the intruder to the
neural networks. Different types of normalization methods were applied on the
input patterns to speed up the learning process. The full 10% KDD Cup 99 train
dataset and the full correct test dataset are used in this work. The results of the
proposed techniques show that there is an improvement in the performance
comparing to the standard techniques, furthermore the Percentage of Successful
Prediction (PSP) and Cost Per Test (CPT) of neural networks and decision trees
are better than association rules. On the other hand, the training time for neural
network takes longer time than the decision trees.