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Theory and Applications for Advanced Text Mining
Due to the growth of computer technologies and web technologies, we can easily collect and store large amounts of text data. We can believe that the data include useful knowledge. Text mining techniques have been studied aggressively in order to extract the knowledge from the data since late 1990s. Even if many important techniques have been developed, the text mining research field continues to expand for the needs arising from various application fields.
Data Mining Applications in Engineering and Medicine
Data Mining Applications in Engineering and Medicine targets to help data miners who wish to apply different data mining techniques. Data mining generally covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, etc. In this book, most of the areas are covered by describing different applications. This is why you will find here why and how Data Mining can also be applied to the improvement of project management.

Data-Intensive Text Processing with MapReduce
Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever.

Agile Data: Building Data Analytics Applications
Mining data requires a deep investment in people and time. How can you be sure you�re building the right models? What tools help you connect with the customer�s needs? With this hands-on book, you�ll learn a flexible toolset and methodology for building effective analytics applications.

The Data Journalism Handbook
This book is intended to be a useful resource for anyone who thinks that they might be interested in becoming a data journalist, or dabbling in data journalism. This collaborative book coordinated by the European Journalism Centre and the Open Knowledge Foundation aims to answer questions like: Where can I find data? How can I request data? What tools can I use? How can I find stories in data? How can I make data journalism sustainable?
Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data
In this book, the three defining characteristics of Big Data � volume, variety, and velocity, are discussed. You�ll get a primer on Hadoop and how IBM is �hardening� it for the enterprise, and learn when to leverage IBM InfoSphere BigInsights (Big Data at rest) and IBM InfoSphere Streams (Big Data in motion) technologies. Deployment and scaling strategies plus industry use cases are also included in this practical guide.

Large Scale Data Handling in Biology
This text is for scientists and students who are learning computational approaches to biology. The book covers the data storage system, computational approaches to biological problems, an introduction to workflow systems, data mining, data visualization, and tips for tailoring existing data analysis software to individual research needs.

Mining of Massive Datasets
Written by two authorities in database and Web technologies, this book is essential reading for students and practitioners alike. It teaches algorithms that have been used in practice to solve key problems in data mining and includes exercises suitable for students from the advanced undergraduate level and beyond.
New Fundamental Technologies in Data Mining
The book thoroughly acquaints you with the new generation of data mining tools and techniques and shows you how to use them to make better business decisions. It describes techniques for detecting customer behavior patterns useful in formulating marketing, sales and customer support strategies. While database analysts will find more than enough technical information to satisfy their curiosity, technically savvy business and marketing managers will find this book accessible.

An Introduction to Data Mining
Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.
The Fourth Paradigm: Data-Intensive Scientific Discovery
This book presents the first broad look at the rapidly emerging field of data-intensive science, with the goal of influencing the worldwide scientific and computing research communities and inspiring the next generation of scientists. Increasingly, scientific breakthroughs will be powered by advanced computing capabilities that help researchers manipulate and explore massive datasets.
Data Acquisition
The book is intended to be a collection of contributions providing a bird�s eye view of some relevant multidisciplinary applications of data acquisition. While assuming that the reader is familiar with the basics of sampling theory and analog-to-digital conversion, the attention is focused on applied research and industrial applications of data acquisition. Even in the few cases when theoretical issues are investigated, the goal is making the theory comprehensible to a wide, application- oriented, audience.
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