Download Data Mining the Web: Uncovering Patterns in Web Content, by Daniel T. Larose, Zdravko Markov PDF

By Daniel T. Larose, Zdravko Markov

This ebook introduces the reader to tools of information mining on the internet, together with uncovering styles in websites (classification, clustering, language processing), constitution (graphs, hubs, metrics), and utilization (modeling, series research, performance).

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Extra resources for Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage

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6. As the recall values increase with k, the precision interpolated at each standard recall level ρ is computed as the maximum precision in all rows, starting with the first one (from the top) in which the actual recall value is greater than or equal to ρ. 8 1 recall Interpolated precision against recall before and after relevance feedback. 3, the interpolated precision of 1 is computed as the maximum precision on rows 1 to 20. 7 to 1 (maximum precision on rows 4 to 20). 5. For comparison the right side of the figure shows the precision against recall for the ranking produced by Rocchio’s method as described in the section “Relevance Feedback” (the sequence of rk ’s starts with 1, 1, 0, 1, 0, .

The cosine similarity ordering seems more natural, while the distance ranking looks peculiar. , none of the terms used in the representation (the dimensions) occur in those documents]. Strangely, one of the matches with the query (d14 ) is farther from the query than the all-zero vectors. There is a similar situation with cosine similarity: Document vector d12 , with just one nonzero component (the one that matches one of the keywords), has the second-highest score among all the documents, but obviously this is an exception.

These sets can also be determined automatically (the approach is then called pseudorelevance feedback): for example, by assuming that the top 10 documents returned by the original query belong to D+ and the rest to D− . , set γ = 0). Also, not all terms have to be included in the equation. The reason is that terms with high TF may occur in many documents and thus contribute too much to the corresponding component of the query vector. This would shift the focus to unimportant terms and may call up documents that are more irrelevant.

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