Efficient enhancement on cellular automata for data mining
ICS'09 Proceedings of the 13th WSEAS international conference on Systems
Performance comparison of RBF networks and MLPs for classification
AIC'09 Proceedings of the 9th WSEAS international conference on Applied informatics and communications
The relationship of sample size and accuracy in radial basis function networks
WSEAS Transactions on Computers
An effective sampling scheme for better multi-layer perceptrons
AIKED'10 Proceedings of the 9th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Comparing fine-grained source code changes and code churn for bug prediction
Proceedings of the 8th Working Conference on Mining Software Repositories
A strategy in sports betting with the nearest neighbours search and genetic algorithms
Annales UMCS, Informatica
Web content mining for market intelligence acquiring from b2c websites
WISE'06 Proceedings of the 7th international conference on Web Information Systems
Multidirectional knowledge extraction process for creating behavioral personas
Proceedings of the 10th Brazilian Symposium on on Human Factors in Computing Systems and the 5th Latin American Conference on Human-Computer Interaction
DLPR: a distributed locality preserving dimension reduction algorithm
IDCS'12 Proceedings of the 5th international conference on Internet and Distributed Computing Systems
Data Mining User Activity in Free and Open Source Software FOSS/ Open Learning Management Systems
International Journal of Open Source Software and Processes
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Apply powerful Data Mining Methods and Models to Leverage your Data for Actionable Results Data Mining Methods and Models provides: * The latest techniques for uncovering hidden nuggets of information * The insight into how the data mining algorithms actually work * The hands-on experience of performing data mining on large data sets Data Mining Methods and Models: * Applies a "white box" methodology, emphasizing an understanding of the model structures underlying the softwareWalks the reader through the various algorithms and provides examples of the operation of the algorithms on actual large data sets, including a detailed case study, "Modeling Response to Direct-Mail Marketing" * Tests the reader's level of understanding of the concepts and methodologies, with over 110 chapter exercises * Demonstrates the Clementine data mining software suite, WEKA open source data mining software, SPSS statistical software, and Minitab statistical software * Includes a companion Web site, www.dataminingconsultant.com, where the data sets used in the book may be downloaded, along with a comprehensive set of data mining resources. Faculty adopters of the book have access to an array of helpful resources, including solutions to all exercises, a PowerPoint(r) presentation of each chapter, sample data mining course projects and accompanying data sets, and multiple-choice chapter quizzes. With its emphasis on learning by doing, this is an excellent textbook for students in business, computer science, and statistics, as well as a problem-solving reference for data analysts and professionals in the field. An Instructor's Manual presenting detailed solutions to all the problems in the book is available onlne.