Name
Abstract | ||
|---|---|---|
Although frequent pattern mining techniques have been extensively studied, the extension of their application onto data streams has been challenging. Due to data streams being continuous and unbounded, an efficient algorithm that avoids multiple scans of data is needed. In this paper we propose Kernel-Tree (KerTree), a single pass tree structured technique that mines frequent patterns in a data stream based on forecasting the support of current items in the future state. Unlike previous techniques that build a tree based on the support of items in the previous block, KerTree performs an estimation of item support in the next block and builds the tree based on the estimation. By building the tree on an estimated future state, KerTree effectively reduces the need to restructure for every block and thus results in a better performance and mines the complete set of frequent patterns from the stream while maintaining a compact structure. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-35101-3_52 | Australasian Conference on Artificial Intelligence |
Keywords | Field | DocType |
frequent pattern mining technique,single pass tree,mines frequent pattern,estimated future state,forecast support,future state,previous block,item support,next block,frequent pattern,data stream,kernel regression,data streams | Single pass,Kernel (linear algebra),Data mining,Data stream mining,Data stream,Computer science,Artificial intelligence,Machine learning,Kernel regression,Tree mining | Conference |
Citations | PageRank | References |
1 | 0.36 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Tse Jung Huang | 1 | 24 | 3.62 |
| Yun Sing Koh | 2 | 393 | 39.52 |
| Gill Dobbie | 3 | 728 | 77.75 |
| Russel Pears | 4 | 205 | 27.00 |