An Efficient Algorithm to Automated Discovery of Interesting Positive and Negative Association Rules
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2015, Vol 6, Issue 6
Abstract
Association Rule mining is very efficient technique for finding strong relation between correlated data. The correlation of data gives meaning full extraction process. For the discovering frequent items and the mining of positive rules, a variety of algorithms are used such as Apriori algorithm and tree based algorithm. But these algorithms do not consider negation occurrence of the attribute in them and also these rules are not in infrequent form. The discovery of infrequent itemsets is far more difficult than their counterparts, that is, frequent itemsets. These problems include infrequent itemsets discovery and generation of interest negative association rules, and their huge number as compared with positive association rules. The interesting discovery of association rules is an important and active area within data mining research. In this paper, an efficient algorithm is proposed for discovering interesting positive and negative association rules from frequent and infrequent items. The experimental results show the usefulness and effectiveness of the proposed algorithm.
Authors and Affiliations
Ahmed Abdul-WahabAl-Opahi, Basheer Al-Maqaleh
Keywords
Related Articles
- EP ID EP122226
- DOI 10.14569/IJACSA.2015.060623
- Views 79
- Downloads 0
Statistical Implicative Similarity Measures for User-based Collaborative Filtering Recommender System
This paper proposes a new similarity measures for User-based collaborative filtering recommender system. The similarity measures for two users are based on the Implication intensity measures. It is called statistical imp...
Load Balancing in Partner-Based Scheduling Algorithm for Grid Workflow
Automated advance reservation has the potential to ensure a good scheduling solution in computational Grids. To improve global throughput of Grid system and enhance resource utilization, workload has to be distributed am...
Blood Diseases Detection using Classical Machine Learning Algorithms
Blood analysis is an essential indicator for many diseases; it contains several parameters which are a sign for specific blood diseases. For predicting the disease according to the blood analysis, patterns that lead to i...
A Novel DDoS Floods Detection and Testing Approaches for Network Traffic based on Linux Techniques
In Today’s Digital World, the continuous interruption of users has affected Web Servers (WSVRs), through Distributed Denial-of-Service (DDoS) attacks. These attacks always remain a massive warning to the World Wide Web (...
Experimental Analysis of the Fault Tolerance of the PIM-SM IP Multicast Routing Protocol under GNS3
PIM-SM is the most commonly used IP multicast routing protocol in IPTV systems. Its fault tolerance is examined by experimenting on a mesh topology multicast test network built up by Cisco routers under GNS3. Different f...