Applied Data Mining for Business and Industry, 2nd edition by Giudici

By Giudici

Utilized information Mining for enterprise and by way of Giudici, Paolo, Figini, Silvia [Wiley,2009] (Paperback) 2d variation [Paperback]

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Extra resources for Applied Data Mining for Business and Industry, 2nd edition

Example text

Finally, the frequencies of 4 and 1 indicate the number of pages that only one of the two navigators visits (PA for presence–absence and AP for absence–presence, where the first letter refers to visitor A and the second to visitor B). The latter two frequencies denote the differential aspects between the two visitors and therefore must be treated in the same way, being symmetrical. The co-presence is aimed at determining the similarity among the two visitors, a fundamental condition because they could belong to the same group.

0 where the generic element dij is a measure of distance between the row vectors xi and xj . The Euclidean distance is the most commonly used distance measure. It is defined, for any two units indexed by i and j , as the square root of the difference between the corresponding vectors, in the p-dimensional Euclidean space: 1/2 p 2 dij = d(xi , xj ) = xis − xj s 2 . s=1 The Euclidean distance can be strongly influenced by a single large difference in one dimension of the values, because the square will greatly magnify that difference.

Multidimensional scaling methods are aimed at representing observations whose observed values are unknown (or not expressed numerically) in a low-dimensional Euclidean space (usually R2 ). The representation is achieved by preserving the original distances as far as possible. 5 explained how to use the method of principal components on a quantitative data matrix in a Euclidean space. It turns the data matrix into a lower-dimensional Euclidean projection by minimising the Euclidean distance between the original observations and the projected ones.

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