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Algorithms and Applications for Academic Search, Recommendation and Quantitative Association Rule Mining

Algorithms and Applications for Academic Search, Recommendation and Quantitative Association Rule Mining presents novel algorithms for;

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Lecture Notes In Data Mining

The continual explosion of information technology and the need for better data collection and management methods has made data mining an;

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Data Algorithms

overview of MapReduce, Hadoop, and Spark. Topics include: Market basket analysis for a large set of transactions Data mining algorithms (K-means;

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Database and Expert Systems Applications

Privacy; Web Search; Data Clustering; Top-K and Skyline Queries; Data Mining and Big Data; Service Computing; Continuous and Temporal Data, and;

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Database and Expert Systems Applications

Privacy; Web Search; Data Clustering; Top-K and Skyline Queries; Data Mining and Big Data; Service Computing; Continuous and Temporal Data, and;

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Action Rules Mining

completely new correlations between attributes and show new possibilities for users. Action rules mining discusses some of data mining and;

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Association Rule Hiding for Data Mining

and appreciate the important aspects of this challenging problem. Association Rule Hiding for Data Mining is designed for researchers;

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Data Mining Concept Animation Library

Library. Potential users for this library can be future students and instructor of a data mining class. The various types of data mining;

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Mining of Massive Datasets

explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing;

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Data Mining and Applications in Genomics

provides a systematic introduction to the use of data mining algorithms as an investigative tool for applications in genomics. Data Mining and;

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Data Mining and Applications in Genomics

provides a systematic introduction to the use of data mining algorithms as an investigative tool for applications in genomics. Data Mining and;

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Foundations of Computational Intelligence 06

techniques for Data Mining. The book is divided into 3 parts: Part-I: Data Click Streams and Temporal Data Mining Part-II: Text and Rule Mining Part;

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Data Mining Algorithms

Researchers and Professionals in data mining and related fields should be familiar with different models and standard algorithms in use to;

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Supervised Descriptive Pattern Mining

This book provides a general and comprehensible overview of supervised descriptive pattern mining, considering classic algorithms and those;

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Advanced Data Mining and Applications

Mining and Applications, ADMA 2013, held in Hangzhou, China, in December 2013. The 32 regular papers and 64 short papers presented in these two;

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Mining User Navigation Patterns from Web Access Log Files

data management, Web mining and Web recommendation as well as Web usage mining for in-depth academic research and industrial development to;

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Data Mining and Knowledge Discovery With Evolutionary Algorithms

knowledge from data. The motivation for applying evolutionary algorithms to data mining is that evolutionary algorithms are robust search methods;

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Database Systems for Advanced Applications

spatial data, top-k and skyline query processing, information retrieval and recommendation, indexing and search systems, cloud computing and;

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Database Systems for Advanced Applications

data; RDF and knowledge graphs; text and data mining; medical data mining; security and privacy; search and information retrieval; query;

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Database Systems for Advanced Applications

data; RDF and knowledge graphs; text and data mining; medical data mining; security and privacy; search and information retrieval; query;

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Principles of Data Mining

This book explains the principal techniques of data mining: for classification, generation of association rules and clustering. It;

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Grid Computing for Bioinformatics and Computational Biology

discipline for academic research and industrial applications, creating a need for the use of Grid computing techniques for large-scale distributed;

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Nature-Inspired Optimization Algorithms

mathematical foundations, techniques for solving discrete and combination optimization problems, data mining techniques and their links to optimization;

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Advances in Swarm Intelligence, Part I

system, differential evolution, neural networks, genetic algorithms, evolutionary computation, fuzzy methods, and hybrid algorithms - for part I;

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Data Mining for Business Analytics

Data Mining for Business Analytics: Concepts, Techniques, and Applications in R presents an applied approach to data mining concepts and;

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Advances in Artificial Intelligence and Its Applications

systems; bioinspired algorithms; neural networks; evolutionary algorithms; fuzzy logic; machine learning and data mining; natural language;

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New Frontier In Evolutionary Algorithms

methodology for a GA-based search strategy with the integration of several Artificial Life and Artificial Intelligence techniques, such as memetic;

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