Name
Abstract | ||
|---|---|---|
We present a generic framework to evaluate patterns obtained from transactional web data streams whose underlying distribution changes with time. The evolving nature of the data makes it very difficult to determine whether there is structure in the data stream, and whether this structure is being learned. This challenge arises in applications such as mining online store transactions, summarizing dynamic document collections, and profiling web traffic. We propose to evaluate this hard instance of unsupervised learning using a continuous assessment of the predictive power of the learned patterns, with specific examples that borrow concepts from supervised learning. We present results from experiments with synthetic data, the 20 Newsgroups dataset, web clickstream data, and a custom collection of RSS News feeds. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/WI-IAT.2009.56 | Web Intelligence |
Keywords | Field | DocType |
intelligent agent,synthetic data,clustering algorithms,face recognition,video,multimedia,unsupervised learning,computer science,distributed computing,supervised learning,testing,data engineering,speaker recognition,social network | Data science,Data mining,Web traffic,Data stream mining,Clickstream,Information retrieval,Computer science,Supervised learning,Synthetic data,Unsupervised learning,Cluster analysis,RSS | Conference |
Volume | ISBN | Citations |
1 | 978-1-4244-5331-3 | 0 |
PageRank | References | Authors |
0.34 | 16 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rojas, Carlos | 1 | 0 | 0.34 |
| Olfa Nasraoui | 2 | 1515 | 164.53 |