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This tutorial provides a comprehensive and comparative overview of general techniques for the key topics in the fields of querying, indexing and mining uncertain data. In particular, it identifies the most generic types of probabilistic similarity queries and discusses general algorithmic methods to answer such queries efficiently. In addition, the tutorial sketches probabilistic methods for important data mining applications in the context of uncertain data with special emphasis on probabilistic clustering and probabilistic pattern mining. The intended audience of this tutorial ranges from novice researchers to advanced experts as well as practitioners from any application domain dealing with uncertain data retrieval and mining.<\/jats:p>","DOI":"10.14778\/1920841.1921066","type":"journal-article","created":{"date-parts":[[2014,6,24]],"date-time":"2014-06-24T12:17:57Z","timestamp":1403612277000},"page":"1653-1654","source":"Crossref","is-referenced-by-count":7,"title":["Similarity search and mining in uncertain databases"],"prefix":"10.14778","volume":"3","author":[{"given":"Matthias","family":"Renz","sequence":"first","affiliation":[{"name":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen, M\u00fcnchen, Germany"}]},{"given":"Reynold","family":"Cheng","sequence":"additional","affiliation":[{"name":"University of Hong Kong, Hong Kong"}]},{"given":"Hans-Peter","family":"Kriegel","sequence":"additional","affiliation":[{"name":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen, M\u00fcnchen, Germany"}]}],"member":"320","published-online":{"date-parts":[[2010,9]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/38714.38724"},{"key":"e_1_2_1_2_1","first-page":"1151","volume-title":"Proceedings of the 32nd International Conference on Very Large Data Bases","author":"Agrawal P.","year":"2006","unstructured":"P. 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