Home Structured Design • Download PDF by Pavel Zezula, Giuseppe Amato, Vlastislav Dohnal, Michal: Similarity Search: The Metric Space Approach

Download PDF by Pavel Zezula, Giuseppe Amato, Vlastislav Dohnal, Michal: Similarity Search: The Metric Space Approach

By Pavel Zezula, Giuseppe Amato, Vlastislav Dohnal, Michal Batko

ISBN-10: 0387291466

ISBN-13: 9780387291468

ISBN-10: 0387291512

ISBN-13: 9780387291512

Within the details Society, info holds the grasp key to monetary impression. Similarity seek: The Metric house strategy will specialize in effective how one can find user-relevant details in collections of items, the similarity of that is quantified utilizing a pairwise distance degree. This ebook is an immediate reaction to fresh advances in computing, communications and garage that have resulted in the present flood of electronic libraries, information warehouses and the unlimited heterogeneity of net assets.

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Extra resources for Similarity Search: The Metric Space Approach

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Intuitively, the rationale for performing such a mapping is that distances in the embedding space approximate the distances between objects in the original space but searching the embedding space is less expensive. 1. We use a transformation function / , such that the distances in the embedded vector space form lower bounds on the distances in the original metric space. The range query R{q^r) evaluation process is modified as follows. ) The query object q is transformed using the function / into a vector q = f(q) in the embedded space.

Chapter 4 presents some of the most relevant in detail. To get some flavor of them, a trivial early termination strategy may involve simply stopping the similarity search algorithm after a certain percentage of the dataset has been accessed, or after a specified time has elapsed. In either case, some qualifying objects may obviously escape detection. 17. The dataset is divided into three subsets, distinguished by the white, black, and gray points bounded by regions TZi,TZ2, and 7^,3. In the example, the query region overlaps all three data regions, so all of them are accessed by the precise similarity search algorithm.

1 Lower-Bounding Functions Having given a definition for lower-bounding distance functions, we provide the reader with examples of some of the most common ones. When necessary, a scaling factor is provided. 37 Foundations of metric space searching Lp Norms. Any Lp norm is a lower-bounding distance function for all Lp/ norms with p' > p. For example, let F — R x E be a 2-dimensional vector space. y) = Y (xi - yif + (x2 - y2)^ , dL^{x,y) = \xi - yi\ + \x2 - y2\' It is apparent that the Li metric function will always be bigger than L2 for the same pair of vectors given as arguments.

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Similarity Search: The Metric Space Approach by Pavel Zezula, Giuseppe Amato, Vlastislav Dohnal, Michal Batko


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