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• W2339316428 abstract "number of users are increasing exponentially so proper analysis of such data by devising efficient algorithms is essential which ultimately helps in determining the life time value of customers and judging the effectiveness of promotional campaigns as well. Better services and quality can be provided by mining the web access log files. In this paper,we have shown that with the help of clustering techniques, Self Organized Feature Maps and K-Means useful knowledge is extracted. We have also proposed to derive the interest and behavior of a significant group of users by applying the concept of Aggregate Usage Profile. Further, this technique has been used for looking frequently accessed pages for recommendations. KeywordsUsage Mining, K-Means, Self-Organizing Feature Maps" @default.
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• W2339316428 date "2011-12-28" @default.
• W2339316428 modified "2023-09-22" @default.
• W2339316428 title "Aggregate Profiling for Recommendation of web pages using SOM and K-Means Clustering Techniques" @default.
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