Description : Mining Memory examines how twentieth-century narratives and films reimagine the self and the nation by representing child and adolescent protagonists and their evolution. The book shows that beyond representing the struggles of individual subjects, narratives of childhood are part of a process of constructing and reconstructing cultural identity.
Description : This book covers the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. The book is organized in three parts. Part I introduces concepts. Part II describes and demonstrates basic data mining algorithms. It also contains chapters on a number of different techniques often used in data mining. Part III focuses on business applications of data mining.
Description : In today's competitive market, experience alone is not enough– job seekers must stand-out and be able to sell themselves in resumes and interviews. Memory Mining provides that necessary tool for digging out gems from past good work then helps refine and polish those into quality usable nuggets which communicate value to an employer.
Description : This collection examines historical and contemporary social, economic, and environmental impacts of mining on Aboriginal communities in northern Canada. Combining oral history research with intensive archival study, this work juxtaposes the perspectives of government and industry with the perspectives of local communities. CONTRIBUTORS: Patricia Boulter, Jean-Sébastien Boutet, Emilie Cameron, Sarah Gordon, Heather Green, Jane Hammond, Joella Hogan, Arn Keeling, Tyler Levitan, Hereward Longley, Scott Midgley, Kevin O'Reilly, Andrea Procter, John Sandlos, and Alexandra Winton.
Description : With big data analytics comes big insights intoprofitability Big data is big business. But having the data and thecomputational power to process it isn't nearly enough to producemeaningful results. Big Data, Data Mining, and Machine Learning:Value Creation for Business Leaders and Practitioners is acomplete resource for technology and marketing executives lookingto cut through the hype and produce real results that hit thebottom line. Providing an engaging, thorough overview of thecurrent state of big data analytics and the growing trend towardhigh performance computing architectures, the book is adetail-driven look into how big data analytics can be leveraged tofoster positive change and drive efficiency. With continued exponential growth in data and ever morecompetitive markets, businesses must adapt quickly to gain everycompetitive advantage available. Big data analytics can serve asthe linchpin for initiatives that drive business, but only if theunderlying technology and analysis is fully understood andappreciated by engaged stakeholders. This book provides a view intothe topic that executives, managers, and practitioners require, andincludes: A complete overview of big data and its notablecharacteristics Details on high performance computing architectures foranalytics, massively parallel processing (MPP), and in-memorydatabases Comprehensive coverage of data mining, text analytics, andmachine learning algorithms A discussion of explanatory and predictive modeling, and howthey can be applied to decision-making processes Big Data, Data Mining, and Machine Learning providestechnology and marketing executives with the complete resource thathas been notably absent from the veritable libraries of publishedbooks on the topic. Take control of your organization's big dataanalytics to produce real results with a resource that iscomprehensive in scope and light on hyperbole.
Description : "A collection of new studies dedicated to Professor Beno Rothenberg, focused on copper in antiquity in the Near East, the eastern Mediterranean, and the British Isles"--Provided by publisher.
Description : Mining Very Large Databases with Parallel Processing addresses the problem of large-scale data mining. It is an interdisciplinary text, describing advances in the integration of three computer science areas, namely `intelligent' (machine learning-based) data mining techniques, relational databases and parallel processing. The basic idea is to use concepts and techniques of the latter two areas - particularly parallel processing - to speed up and scale up data mining algorithms. The book is divided into three parts. The first part presents a comprehensive review of intelligent data mining techniques such as rule induction, instance-based learning, neural networks and genetic algorithms. Likewise, the second part presents a comprehensive review of parallel processing and parallel databases. Each of these parts includes an overview of commercially-available, state-of-the-art tools. The third part deals with the application of parallel processing to data mining. The emphasis is on finding generic, cost-effective solutions for realistic data volumes. Two parallel computational environments are discussed, the first excluding the use of commercial-strength DBMS, and the second using parallel DBMS servers. It is assumed that the reader has a knowledge roughly equivalent to a first degree (BSc) in accurate sciences, so that (s)he is reasonably familiar with basic concepts of statistics and computer science. The primary audience for Mining Very Large Databases with Parallel Processing is industry data miners and practitioners in general, who would like to apply intelligent data mining techniques to large amounts of data. The book will also be of interest to academic researchers and postgraduate students, particularly database researchers, interested in advanced, intelligent database applications, and artificial intelligence researchers interested in industrial, real-world applications of machine learning.
Description : This book constitutes the proceedings of the 10th International Conference on Advanced Data Mining and Applications, ADMA 2014, held in Guilin, China during December 2014. The 48 regular papers and 10 workshop papers presented in this volume were carefully reviewed and selected from 90 submissions. They deal with the following topics: data mining, social network and social media, recommend systems, database, dimensionality reduction, advance machine learning techniques, classification, big data and applications, clustering methods, machine learning, and data mining and database.
Description : Managing and Mining Uncertain Data, a survey with chapters by a variety of well known researchers in the data mining field, presents the most recent models, algorithms, and applications in the uncertain data mining field in a structured and concise way. This book is organized to make it more accessible to applications-driven practitioners for solving real problems. Also, given the lack of structurally organized information on this topic, Managing and Mining Uncertain Data provides insights which are not easily accessible elsewhere. Managing and Mining Uncertain Data is designed for a professional audience composed of researchers and practitioners in industry. This book is also suitable as a reference book for advanced-level students in computer science and engineering, as well as the ACM, IEEE, SIAM, INFORMS and AAAI Society groups.
Description : This book constitutes the refereed proceedings of the 8th Pacific-Asia Conference on Knowledge Discovery and Data mining, PAKDD 2004, held in Sydney, Australia in May 2004. The 50 revised full papers and 31 revised short papers presented were carefully reviewed and selected from a total of 238 submissions. The papers are organized in topical sections on classification; clustering; association rules; novel algorithms; event mining, anomaly detection, and intrusion detection; ensemble learning; Bayesian network and graph mining; text mining; multimedia mining; text mining and Web mining; statistical methods, sequential data mining, and time series mining; and biomedical data mining.