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Similarity Search is the most general term used for a range of mechanisms which share the principle of searching (typically, very large) spaces of objects where the only available comparator is the similarity between any pair of objects. This is becoming increasingly important in an age of large information repositories where the objects contained do not possess any natural order, for example large collections of images, sounds and other sophisticated digital objects. Nearest neighbor search and Range queries are important subclasses of similarity search, and a number of solutions exist. Research in Similarity Search is dominated by the inherent problems of searching over complex objects. Such objects cause most known techniques to lose traction over large collections, and there are still many unsolved problems. Unfortunately, in many cases where similarity search is necessary, the objects are inherently complex. The most general approach to similarity search that allows construction of efficient index structures use the mathematical notion of Metric space. ==Bibliography== *Pei Lee, Laks V. S. Lakshmanan, Jeffrey Xu Yu: On Top-k Structural Similarity Search. ICDE 2012:774-785 *Zezula, P., Amato, G., Dohnal, V., and Batko, M. Similarity Search - The Metric Space Approach. Springer, 2006. ISBN 0-387-29146-6 *Samet, H.. Foundations of Multidimensional and Metric Data Structures. Morgan Kaufmann, 2006. ISBN 0-12-369446-9 *E. Chavez, G. Navarro, R.A. Baeza-Yates, J.L. Marroquin, (Searching in metric spaces ), ACM Computing Surveys (CSUR), 2001 *M.L. Hetland, (The Basic Principles of Metric Indexing ), Swarm Intelligence for Multi-objective Problems in Data Mining, Studies in Computational Intelligence Volume 242, 2009, pp 199–232 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Similarity search」の詳細全文を読む スポンサード リンク
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