Name
Title | ||
|---|---|---|
estWin: adaptively monitoring the recent change of frequent itemsets over online data streams |
Abstract | ||
|---|---|---|
Knowledge embedded in a data stream is likely to be changed as time goes by. Consequently, identifying the recent change of the knowledge quickly can provide valuable information for the analysis of the data stream. However, most of mining algorithms or frequency approximation algorithms for a data stream do not able to extract the recent change of information in a data stream adaptively. This paper proposes a sliding window-based method that finds recently frequent itemsets over an online data stream adaptively. The size of a window defines a desired life-time of the information in a newly generated transaction. Consequently, only recently generated transactions in the range of the window are considered to find the frequent itemsets of a data stream. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1145/956863.956967 | International Conference on Information and Knowledge Management |
Keywords | Field | DocType |
frequent itemsets,window-based method,online data stream adaptively,recent change,mining algorithm,data stream adaptively,frequency approximation algorithm,data stream,valuable information,sliding window,data streams,pruning | Approximation algorithm,Data mining,Data stream mining,Data stream clustering,Sliding window protocol,Computer science,Adaptive method,Data stream,Real-time computing,Database transaction,Data flow diagram | Conference |
ISBN | Citations | PageRank |
1-58113-723-0 | 21 | 0.93 |
References | Authors | |
5 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joong Hyuk Chang | 1 | 401 | 19.81 |
| Won Suk Lee | 2 | 536 | 51.26 |