DocumentCode :
1867748
Title :
Online Evaluation of Patterns from Evolving Web Data Streams
Author :
Rojas, Carlos ; Nasraoui, Olfa
Volume :
1
fYear :
2009
fDate :
15-18 Sept. 2009
Firstpage :
315
Lastpage :
318
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.
Keywords :
Clustering algorithms; Computer science; Conferences; Data engineering; Distributed computing; Intelligent agent; Supervised learning; Testing; USA Councils; Unsupervised learning;
fLanguage :
English
Publisher :
iet
Conference_Titel :
Web Intelligence and Intelligent Agent Technologies, 2009. WI-IAT '09. IEEE/WIC/ACM International Joint Conferences on
Conference_Location :
Milan, Italy
Print_ISBN :
978-0-7695-3801-3
Electronic_ISBN :
978-1-4244-5331-3
Type :
conf
DOI :
10.1109/WI-IAT.2009.56
Filename :
5286055
Link To Document :
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