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	Browse All Reviews > Information Systems (H) > Database Management (H.2) > Database Applications (H.2.8) > Data Mining (H.2.8...)     Options: All Media Types Journals Proceedings Div Books Whole Books Other Date Reviewed Title Author Publisher Published Date Descending Ascending     1-10 of 276 Reviews about "Data Mining (H.2.8...)": Date Reviewed   Methods for finding frequent items in data streams Cormode G., Hadjieleftheriou M.  The VLDB Journal: The International Journal on Very Large Data Bases 19(1): 3-20, 2010. Type: Article A large class of real-world applications, with a tight relation to large-scale industrial systems, requires the processing of data that arrives in the form of streams. Data stream mining is a challenging research problem due to the high volume of ... Aug 10 2010   Computational social network analysis: trends, tools and research advances Abraham A., Hassanien A., Snsel V.,  Springer Publishing Company, Incorporated, Dordrecht, Netherlands, 2009. 485 pp. Type: Book Designed for advanced students of intelligent social networks, this book consists of almost 500 pages and 18 chapters--an ideal length for a traditional one-semester course. The chapters are divided into three parts: “Social Network... Jul 30 2010   Web 2.0 & semantic Web Devedſić V., Gašević D.,  Springer Publishing Company, Incorporated, New York, NY, 2009. 201 pp. Type: Book This volume contains eight research papers on the relation between Web 2.0 and the semantic Web. The book has an advanced theoretical approach. It is addressed to specialists in Web development, but it will also benefit anyone interested in... Jul 16 2010    Scientific data mining and knowledge discovery: principles and foundations Gaber M.,  Springer Publishing Company, Incorporated, New York, NY, 2009. 400 pp. Type: Book Much of scientific discovery is the result of induction--patterns peeking through data are recognized. Of course, this process requires a trained mind and a conceptual framework that may be either confirmed or challenged. The investigator... Jul 15 2010   An efficient method for generating, storing and matching features for text mining Chan S., Lam W.  PAKDD 2009 (Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, Bangkok, Thailand,  Apr 27-30, 2009) 86-97, 2009. Type: Proceedings When developing useful text mining applications, feature extraction is a key technique. Over the last decade, conditional random fields (CRFs) have proven to be efficient for segmentation and labeling sequence data.... Jun 24 2010    Data engineering: mining, information and intelligence Chan Y., Talburt J., Talley T.,  Springer Publishing Company, Incorporated, New York, NY, 2009. 447 pp. Type: Book What attracted me to this book was its title. With the massive growth of data that spans many different types, including multimedia, and runs into petabytes and beyond in volume, the effective management of data is now a major concern. Data... Jun 22 2010   An adaptive threshold framework for event detection using HMM-based life profiles Chen C., Chen M., Chen M.  ACM Transactions on Information Systems 27(2): 1-35, 2009. Type: Article The goal of topic detection and tracking (TDT) systems is to organize the temporally ordered documents of a news stream according to events, in order to avoid overwhelming news consumers with too many unconnected stories.... Jun 21 2010   An event-based framework for characterizing the evolutionary behavior of interaction graphs Asur S., Parthasarathy S., Ucar D.  ACM Transactions on Knowledge Discovery from Data (TKDD) 3(4): 1-36, 2009. Type: Article The behavior of interactions can be analyzed with the help of interaction graphs that represent the existence of relationships between entities. Temporal change of relationships is investigated by comparing consecutive snapshots of the graph,... Jun 10 2010   Genetic programming theory and practice VII Riolo R., O’Reilly U., McConaghy T.,  Springer Publishing Company, Incorporated, New York, NY, 2009. 231 pp. Type: Book Genetic programming is a methodology that uses evolutionary models to uncover the most suitable programs for problem solving. The program itself evolves and changes, which is very different from the conventional deterministic model where the... May 28 2010   A rough set approach to feature selection based on ant colony optimization Chen Y., Miao D., Wang R.  Pattern Recognition Letters 31(3): 226-233, 2010. Type: Article Chen, Miao, and Wang describe a novel heuristic algorithm for feature selection. The algorithm combines rough set theory (RST) with ant colony optimization (ACO). An information-based feature-selection algorithm determines a feature core, and the ... May 25 2010       Display 510152550100 per page
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