NDLI: Dependable performance analysis for fuzzy clustering of web usage data

Content Provider	IEEE Xplore Digital Library
Author	Ketata, A. Mudur, S. Shiri, N.
Copyright Year	2009
Abstract	Fuzzy clustering is a popular method for modeling web usage data, and a number of techniques have been proposed. Performance of such techniques has been demonstrated through experiments using datasets which are often limited in the size and/or variety. This is mainly due to the difficulty in acquiring large real data, and also to the huge amount of time and effort required in performing experiments. We investigate ways to ensure dependability of such results and their analyses. For this we consider three issues. First we need to ensure that the clustering quality indices used for comparing different techniques are not biased towards any parameter specific to any of them. Second, more ground truth is provided by measuring the quality through an application of the usage model than through the clustering quality index alone. Third, given the limited data sets and experiments, use of statistical significance testing can provide more confidence in that the results obtained are not by mere chance. We present our approach for dependable performance analysis using some well-known fuzzy clustering techniques along with prediction quality used as the application specific metric.
Starting Page	275
Ending Page	282
File Size	745968
Page Count	8
File Format	PDF
ISBN	9781424427659
DOI	10.1109/CIDM.2009.4938660
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-03-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Fuzzy sets Clustering algorithms Web mining Predictive models Performance analysis Recommender systems Testing
Content Type	Text
Resource Type	Article

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