Name
Abstract | ||
|---|---|---|
In this paper, we consider the problem of frequent elements over data stream seeks the set of items whose frequency exceeds 驴N for a given threshold parameter 驴. We refer to this model as the sliding window model. We also use a user specified error parameter, 驴, to control the accuracy of the mining result. We also propose an FIA (Frequent Itemsets mining based on an Approximate counting) algorithm based on the Chernoff bound with a guarantee of the output quality and also a bound on the memory usage. The proposed algorithm show that runs significantly faster and consumes less memory than do existing algorithms for mining approximate frequent itemsets. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-10677-4_35 | ICONIP (1) |
Keywords | Field | DocType |
window sliding,approximate frequent itemsets,threshold parameter,memory usage,chernoff bound,frequent element,approximate counting,proposed algorithm show,data streams,approximate.,frequent itemsets,frequent itemsets mining,mining result,window model,error parameter,approximate,sliding window | Data mining,Data stream mining,Sliding window protocol,Pattern recognition,Computer science,Data stream,Artificial intelligence,Chernoff bound | Conference |
Volume | ISSN | Citations |
5863 | 0302-9743 | 1 |
PageRank | References | Authors |
0.40 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Younghee Kim | 1 | 19 | 3.33 |
| Joonsuk Ryu | 2 | 1 | 0.74 |
| Ungmo Kim | 3 | 58 | 11.90 |