By Guandong Xu

Info mining has witnessed huge advances in fresh a long time. New learn questions and sensible demanding situations have arisen from rising components and functions in the quite a few fields heavily regarding human lifestyle, e.g. social media and social networking. This booklet goals to bridge the space among conventional information mining and the newest advances in newly rising info prone. It explores the Read more...

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The aim of this chapter is to lay down a solid foundation for readers to better understand the techniques and algorithms mentioned in later chapters. 44 Applied Data Mining References [1] Chandrika Kamath. Scientific Data Mining: A Practical Perspective, Lawrence Livermore National Laboratory, Livermore, California, 2009. [2] S. Brin and L. Page. Anatomy of a Large-scale Hypertextual Web Search engine, Proc. 7th Intl. World-Wide-Web Conference, pp. 107C117, 1998. [3] Norman Lloyd Johnson, Samuel Kotz, N.

It gauges similarity of an unknown sample set to a known one. It differs from Euclidean distance in that it takes into account the correlations of the data set and is scale-invariant. In other words, it is a multivariate effect size. Formally, the Mahalanobis distance of a multivariate vector x = (x1, x2, x3, · · · , xN)T from a group of values with mean µ = (µ1, µ2, µ3, · · · , µN)T and covariance matrix S is defined as: DM(x) = T −1 μ ). 12) Mahalanobis distance can also be defined as a dissimilarity measure ‚ ‚ between two random vectors x and y of the same distribution with the covariance matrix S: Mathematical Foundations 33 d ( x, y ) = ( x − y )T S −1 ( x − y ).

1 Cosine Similarity In some applications, the classic vector space model is used generally, such as Relevance rankings of documents in a keyword search. It can be calculated, using the assumptions of document similarities theory, by comparing the deviation of angles between each document vector and the original query vector where the query is represented as same kind of vector as the documents. An important problem that arises when we search for similar items of any kind is that there may be far too many pairs of items to test each pair for their degree of similarity, even if computing the similarity of any one pair can be made very easy.

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