Name
Abstract | ||
|---|---|---|
In Data mining field, the primary task is to mine frequent itemsets from a transaction database using Association Rule Mining (ARM). Utility Mining aims to identify itemsets with high utilities by considering profit, quantity, cost or other user preferences. In market basket analysis, high consideration should be given to utility of item in a transaction, since items having low selling frequencies may have high profits. As a result, High Utility Itemset Mining emerged as a revolutionary field in Data Mining. Rare itemsets provide useful information in different decision-making domains. High Utility Rare Itemset Mining, HURI algorithm proposed in [12], generate high utility rare itemsets of users' interest. HURI is a two-phase algorithm, phase I generates rare itemsets and phase 2 generates high utility rare itemsets, according to users' interest. In this paper, performance evaluation and complexity analysis of HURI algorithm, based on different parameters have been discussed which indicates the efficiency of HURI. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-31552-7_54 | ADVANCES IN COMPUTING AND INFORMATION TECHNOLOGY, VOL 2 |
Keywords | Field | DocType |
Association Rule Mining,Utility Mining,Rare itemset,High Utility Rare itemset Mining | Utility mining,Computer science,Algorithm,Association rule learning,Affinity analysis,Database transaction | Conference |
Volume | ISSN | Citations |
177 | 2194-5357 | 2 |
PageRank | References | Authors |
0.36 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jyothi Pillai | 1 | 7 | 1.82 |
| O. P. Vyas | 2 | 121 | 14.28 |
| Maybin K. Muyeba | 3 | 47 | 7.61 |