H-Map-Based Technique for Mining High Average Utility Itemset

ABSTRACT

High Utility Itemset Mining (HUIM) is the process of locating itemsets that are profitable and useful to users. One of the key flaws in HUIM is that as the length of the itemset increases, the utility also increases. The true utility/profit of the itemset is not revealed in HUIM. High Average Utility Itemset mining overcomes the limitations of HUIM by taking the length of the itemset into account when estimating the utility. Existing pruning methods used for eliminating weak candidates overestimate the average usefulness of itemsets, causing the mining process to slow down. To prevent processing unpromising candidate itemsets and efficiently reduce the search space and processing time, the proposed methodology employs Upper Bound using Remaining Items Utility, Maximum Itemset Utility, and Sum of Maximum Utility in a Transaction. It also uses the multithreaded parallel approach to reduce the processing time. The H-Map-based data structure (H-Map) used for storing the utility values reduces the lookup time and joins used for itemset extension compared to existing state-of-the-art High Average Utility Itemset mining algorithms. The performance of the proposed work is evaluated in terms of memory usage and the time taken for processing. The proposed work increases the overall efficiency of the system employing effective pruning algorithms for pruning poor candidate itemsets and an efficient data structure for storing utility values.

KEYWORDS:

• Frequent itemset mining
• High average utility itemset mining
• High utility itemset mining
• Itemset mining
• Pruning strategy
• Upper bounds

Acknowledgments

The authors thank the Mepco Schlenk Engineering college management for their support in carrying out the research work.

Additional information

Notes on contributors

M. S. Bhuvaneswari

M S Bhuvaneswari received her PhD degree from Anna University, Chennai, Tamil Nadu, India. She is currently working as assistant professor, selection grade with the Department of Computer Science and Engineering. Her current research interests include web mining, sequential pattern mining and recommendation systems.

N. Balaganesh

N Balaganesh received his PhD degree from Anna University, Chennai, Tamil Nadu, India. He is currently working as assistant professor, selection grade, with the Department of Computer Science and Engineering. His current research interests include web mining, opinion mining, and recommendation systems. Email: [email protected]

K. Muneeswaran

K Muneeswaran received his PhD degree from Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India. He is currently working as senior professor and heading the Department of Computer Science and Engineering. His current research interests include web mining, text mining, network security, recommendation systems and image processing. To add to his credits, he has published numerous papers in reputed journals. Email: [email protected]