Name
Abstract | ||
|---|---|---|
Ubiquitous data stream mining (UDSM) is the process of performing data analysis on mobile, embedded and ubiquitous devices. In many cases, a large volume of data can be mined for interesting and relevant information in a wide variety of applications. Data stream mining requires computationally intensive mining techniques to be applied in mobile environments constrained by analysis of a real-time single pass with limited computational resources. Therefore, we have to ensure that the result is within the error tolerance range. In this paper, we suggest a method for a false-negative approach based on the Chernoff bound for efficient analysis of the data stream. Hence, we consider the problem of approximating frequency counts for space-efficient computation over data stream sliding windows. We show that a false-negative approach allowing a controlled number of frequent itemsets to be missing from the output is a more promising solution for mining frequent itemsets from a ubiquitous data stream. These are simple to implement, and have provable quality, space, and time guarantees. The experimental results have shown that the proposed algorithms achieve a high accuracy of at least 99% and require a small execution time. Copyright © 2011 John Wiley & Sons, Ltd. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1002/dac.1211 | Int. J. Communication Systems |
Keywords | Field | DocType |
data analysis,frequent itemsets,data stream mining,ubiquitous data stream analysis,false-negative approach,window-based false-negative approach,ubiquitous device,computationally intensive mining technique,ubiquitous data stream,ubiquitous data stream mining,efficient analysis,data stream,chernoff bound | Data mining,Data stream mining,Sliding window protocol,Data stream,Computer science,Error tolerance,Real-time computing,Execution time,Data stream analysis,Chernoff bound,Computation | Journal |
Volume | Issue | ISSN |
25 | 6 | 1074-5351 |
Citations | PageRank | References |
4 | 0.48 | 19 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Younghee Kim | 1 | 19 | 3.33 |
| Doosoon Park | 2 | 134 | 27.09 |
| Heewan Kim | 3 | 5 | 0.83 |
| Ungmo Kim | 4 | 58 | 11.90 |