Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book is for those Splunk developers who want to learn advanced strategies to deal with big data from an enterprise architectural perspective. You need to have good working knowledge of Splunk.

Data mining --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Computer programs.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Data Mining Applications with R is a great resource for researchers and professionals to understand the wide use of R, a free software environment for statistical computing and graphics, in solving different problems in industry. R is widely used in leveraging data mining techniques across many different industries, including government, finance, insurance, medicine, scientific research and more. Twenty different real-world case studies illustrate various techniques in rapidly growing areas, including: RetailCrime and homeland securityStock mark

Data mining --- R (Computer program language) --- Industrial applications --- GNU-S (Computer program language) --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Domain-specific programming languages --- Database searching

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it focuses on providing a synthesis that explains the most important machine learning algorithms in a quantum framework. Theoretical advances in quantum computing are hard to follow for computer scientists, and sometimes even for researchers involved in the field. The lack of a step-by-step guide hampers the broader understanding of this emergent interdisciplinary body of research. Quantum Machine L

Data mining. --- Machine learning. --- Quantum theory. --- Engineering & Applied Sciences --- Computer Science --- Machine learning --- Data mining --- Quantum theory --- Mathematical models. --- Data processing. --- Quantum dynamics --- Quantum mechanics --- Quantum physics --- Physics --- Mechanics --- Thermodynamics --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Learning, Machine --- Artificial intelligence --- Machine theory

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

A concise, hands-on guide with many practical examples and a detailed treatise on inference and social science research that will help you in mining data in the real world. Whether you are an undergraduate who wishes to get hands-on experience working with social data from the Web, a practitioner wishing to expand your competencies and learn unsupervised sentiment analysis, or you are simply interested in social data analysis, this book will prove to be an essential asset. No previous experience with R or statistics is required, though having knowledge of both will enrich your experience.

Data mining -- Industrial applications -- Case studies. --- Embedded computer systems. --- Multimedia systems. --- R (Computer program language). --- Engineering & Applied Sciences --- Computer Science --- Data mining. --- Social media. --- User-generated media --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Communication --- User-generated content --- Database searching --- GNU-S (Computer program language) --- Domain-specific programming languages

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Big Data is made up of lots of little data: numbers entered into cell phones, addresses entered into GPS devices, visits to websites, online purchases, ATM transactions, and any other activity that leaves a digital trail. Although the abuse of Big Data -- surveillance, spying, hacking -- has made headlines, it shouldn't overshadow the abundant positive applications of Big Data. In Reality Mining, Nathan Eagle and Kate Greene cut through the hype and the headlines to explore the positive potential of Big Data, showing the ways in which the analysis of Big Data ("Reality Mining") can be used to improve human systems as varied as political polling and disease tracking, while considering user privacy.Eagle, a recognized expert in the field, and Greene, an experienced technology journalist, describe Reality Mining at five different levels: the individual, the neighborhood and organization, the city, the nation, and the world. For each level, they first offer a nontechnical explanation of data collection methods and then describe applications and systems that have been or could be built. These include a mobile app that helps smokers quit smoking; a workplace "knowledge system"; the use of GPS, Wi-Fi, and mobile phone data to manage and predict traffic flows; and the analysis of social media to track the spread of disease. Eagle and Greene argue that Big Data, used respectfully and responsibly, can help people live better, healthier, and happier lives.

Data mining. --- Big data. --- Computer networks --- Information science --- Social aspects. --- Statistical methods. --- Informetrics --- Data sets, Large --- Large data sets --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Communication --- Information literacy --- Library science --- Database searching --- Data sets