Name
Abstract | ||
|---|---|---|
Frequent itemset mining aims at discovering patterns the supports of which are beyond a given threshold. In many applications, including network event management systems, which motivated this work, patterns are composed of items each described by a subset of attributes of a relational table. As it involves an exponential mining space, the efficient implementation of user preferences and mining constraints becomes the first priority for a mining algorithm. User preferences and mining constraints are often expressed using patterns’ attribute structures. Unlike traditional methods that mine all frequent patterns indiscriminately, we regard frequent itemset mining as a two-step process: the mining of the pattern structures and the mining of patterns within each pattern structure. In this paper, we present a novel architecture that uses pattern structures to organize the mining space. In comparison with the previous techniques, the advantage of our approach is two-fold: (i) by exploiting the interrelationships among pattern structures, execution times for mining can be reduced significantly; and (ii) more importantly, it enables us to incorporate high-level simple user preferences and mining constraints into the mining process efficiently. These advantages are demonstrated by our experiments using both synthetic and real-life datasets. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/s10115-004-0170-9 | Knowl. Inf. Syst. |
Keywords | Field | DocType |
mining constraint,data mining,user preference,mining space,database integration,frequent itemset mining,association rule mining,exponential mining space,mining process,frequent patterns indiscriminately,pattern structure,mining algorithm,high-level simple user preference,demand-driven frequent itemset mining,management system | Information integration,Data mining,Concept mining,Data stream mining,Computer science,Molecule mining,Association rule learning,Information extraction,Artificial intelligence,Knowledge extraction,Machine learning,K-optimal pattern discovery | Journal |
Volume | Issue | ISSN |
8 | 1 | 0219-3116 |
Citations | PageRank | References |
9 | 0.72 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Heng Wang | 1 | 5539 | 275.36 |
| Chang-Shing Perng | 2 | 478 | 35.92 |
| Sheng Ma | 3 | 1139 | 76.32 |
| Philip S. Yu | 4 | 30670 | 3474.16 |