Name
Title | ||
|---|---|---|
Self-sufficient itemsets: An approach to screening potentially interesting associations between items |
Abstract | ||
|---|---|---|
Self-sufficient itemsets are those whose frequency cannot be explained solely by the frequency of either their subsets or of their supersets. We argue that itemsets that are not self-sufficient will often be of little interest to the data analyst, as their frequency should be expected once that of the itemsets on which their frequency depends is known. We present tests for statistically sound discovery of self-sufficient itemsets, and computational techniques that allow those tests to be applied as a post-processing step for any itemset discovery algorithm. We also present a measure for assessing the degree of potential interest in an itemset that complements these statistical measures. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1145/1644873.1644876 | TKDD |
Keywords | Field | DocType |
itemset discovery,post-processing step,sound discovery,data analyst,itemset screening,interesting association,association rules,statistical measure,computational technique,statistical evaluation,self-sufficient itemsets,additional key words and phrases: association discovery,present test,potential interest,itemset discovery algorithm,association rule,statistical test | Data mining,Computer science,Association rule learning,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
4 | 1 | 1556-4681 |
Citations | PageRank | References |
28 | 1.07 | 26 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Geoffrey I. Webb | 1 | 3130 | 234.10 |