Name
Abstract | ||
|---|---|---|
A new algorithm for minimal infrequent itemset mining is presented. Potential applications of finding infrequent itemsets include statistical disclosure risk assessment, bioinformatics, and fraud detection. This is the first algorithm designed specifically for finding these rare itemsets. Many itemset properties used implicitly in the algorithm are proved. The problem is shown to be NP-complete. Experimental results are then presented. |
Year | Venue | Field |
|---|---|---|
2007 | DMIN | Data mining,Computer science,Risk assessment |
DocType | Citations | PageRank |
Conference | 19 | 1.00 |
References | Authors | |
8 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David J. Haglin | 1 | 112 | 19.45 |
| Anna M. Manning | 2 | 39 | 3.97 |