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Data Mining
Data mining is a branch of computer science that is used to automatically extract meaningful, useful knowledge and previously unknown, hidden, interesting patterns from a large amount of data to support the decision-making process. This book presents recent theoretical and practical advances in the field of data mining. It discusses a number of data mining methods, including classification, clustering, and association rule mining. This book brings together many different successful data mining studies in various areas such as health, banking, education, software engineering, animal science, and the environment.
Data Science and Big Data Computing
This illuminating text/reference surveys the state of the art in data science, and provides practical guidance on big data analytics. Expert perspectives are provided by authoritative researchers and practitioners from around the world, discussing research developments and emerging trends, presenting case studies on helpful frameworks and innovative methodologies, and suggesting best practices for efficient and effective data analytics. Features: reviews a framework for fast data applications, a technique for complex event processing, and agglomerative approaches for the partitioning of networks; introduces a unified approach to data modeling and management, and a distributed computing perspective on interfacing physical and cyber worlds; presents techniques for machine learning for big data, and identifying duplicate records in data repositories; examines enabling technologies and tools for data mining; proposes frameworks for data extraction, and adaptive decision making and social media analysis.
Data Mining
This book on data mining explores a broad set of ideas and presents some of the state-of-the-art research in this field. The book is triggered by pervasive applications that retrieve knowledge from real-world big data. Data mining finds applications in the entire spectrum of science and technology including basic sciences to life sciences and medicine, to social, economic, and cognitive sciences, to engineering and computers. The chapters discuss various applications and research frontiers in data mining with algorithms and implementation details for use in real-world. This can be through characterization, classification, discrimination, anomaly detection, association, clustering, trend or evolution prediction, etc. The intended audience of this book will mainly consist of researchers, research students, practitioners, data analysts, and business professionals who seek information on the various data mining techniques and their applications.
New Fundamental Technologies in Data Mining
The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining.
Developing and Monitoring Smart Environments for Intelligent Cities
In recent years, intelligent cities, also known as smart cities or cognitive cities, have become a perceived solution for improving the quality of life of citizens while boosting the efficiency of city services and processes. This new vision involves the integration of various sectors of society through the use of the internet of things. By continuing to enhance research for the better development of the smart environments needed to sustain intelligent cities, citizens will be empowered to provision the e-services provided by the city, city officials will have the ability to interact directly with the community as well as monitor digital environments, and smart communities will be developed ...
Artificial Intelligence Research and Development
Since it was formed in 1994, the Catalan Association for Artificial Intelligence (ACIA) has been promoting cooperation between researchers in artificial intelligence within the Catalan speaking community. The association now holds an annual conference in the Catalan region, which aims to foster discussion of the latest developments in artificial intelligence within the community of Catalan countries, as well as amongst members of the wider AI community. This book presents the proceedings of the 18th International Conference (CCIA 2015), held in Valencia, Spain, in October 2015. It contains full versions of the peer reviewed papers presented at the conference, as well as shorter poster contributions. In addition to this year’s dominant research trends of classification, decision support systems and data mining, many other topics are covered, ranging from theoretical aspects to descriptions of real applications. This overview of current work in the Catalan artificial intelligence community and of the collaboration between ACIA members and the AI community worldwide will be of interest to all those working in the field of artificial intelligence.
Recent Trends in Wireless and Mobile Networks
This book constitutes the refereed proceedings of the Third International Conference on Wireless, Mobile Networks, WiMo 2011, and of The Third International Conference on Computer Networks and Communications, CoNeCo 2011, held in Ankara, Turkey, in June 2011. The 40 revised full papers presented were carefully reviewed and selected from 202 submissions.
Web 2.0 and Cloud Technologies for Implementing Connected Government
The emergence of cloud computing, internet of things, mobile technologies, and social networking have created better-connected members of the public who are digitally linked with each other in real time. Establishing this two-way interaction between citizens and governments has thus become attractive and an expected feature of governments worldwide. Previously, federal and local governments relied on first-generation technologies to provide basic levels of automation and digitization. Now, because of their desire to become more open, transparent, accountable, and connected, newer technologies including cloud computing, mobile networking, big data analytics, Web 2.0, and social media must be ...
Data Classification and Incremental Clustering in Data Mining and Machine Learning
This book is a comprehensive, hands-on guide to the basics of data mining and machine learning with a special emphasis on supervised and unsupervised learning methods. The book lays stress on the new ways of thinking needed to master in machine learning based on the Python, R, and Java programming platforms. This book first provides an understanding of data mining, machine learning and their applications, giving special attention to classification and clustering techniques. The authors offer a discussion on data mining and machine learning techniques with case studies and examples. The book also describes the hands-on coding examples of some well-known supervised and unsupervised learning techniques using three different and popular coding platforms: R, Python, and Java. This book explains some of the most popular classification techniques (K-NN, Naïve Bayes, Decision tree, Random forest, Support vector machine etc,) along with the basic description of artificial neural network and deep neural network. The book is useful for professionals, students studying data mining and machine learning, and researchers in supervised and unsupervised learning techniques.
