Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Quantifiable data mining using principal component analysis
Quantifiable data mining using principal component analysis
Data mining: concepts and techniques
Data mining: concepts and techniques
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
NetCube: A Scalable Tool for Fast Data Mining and Compression
Proceedings of the 27th International Conference on Very Large Data Bases
A new two-phase sampling based algorithm for discovering association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering actionable patterns in event data
IBM Systems Journal
Hi-index | 0.00 |
This paper presents the Principal Component Analysis (PCA) which is integrated in the proposed architectural model and the utilization of apriori algorithm for association rule discovery. The scope of this study includes techniques such as the use of devised data reduction technique and the deployment of association rule algorithm in data mining to efficiently process and generate association patterns. The evaluation shows that interesting association rules were generated based on the approximated data which was the result of dimensionality reduction, thus, implied rigorous and faster computation than the usual approach. This is attributed to the PCA method which reduces the dimensionality of the original data prior to the processing. Furthermore, the proposed model had verified the premise that it could handle sparse information and suitable for data of high dimensionality as compared to other technique such as the wavelet transform.