Big Data Data Mining and Machine Learning

In this book, Jared Dean offers an accessible and thorough review of the current state of big data analytics and the growing trend toward high performance computing architectures.

Author: Jared Dean

Publisher: John Wiley & Sons

ISBN: 9781118618042

Category: Computers

Page: 288

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With big data analytics comes big insights into profitability Big data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of big data analytics and the growing trend toward high performance computing architectures, the book is a detail-driven look into how big data analytics can be leveraged to foster positive change and drive efficiency. With continued exponential growth in data and ever more competitive markets, businesses must adapt quickly to gain every competitive advantage available. Big data analytics can serve as the linchpin for initiatives that drive business, but only if the underlying technology and analysis is fully understood and appreciated by engaged stakeholders. This book provides a view into the topic that executives, managers, and practitioners require, and includes: A complete overview of big data and its notable characteristics Details on high performance computing architectures for analytics, massively parallel processing (MPP), and in-memory databases Comprehensive coverage of data mining, text analytics, and machine learning algorithms A discussion of explanatory and predictive modeling, and how they can be applied to decision-making processes Big Data, Data Mining, and Machine Learning provides technology and marketing executives with the complete resource that has been notably absent from the veritable libraries of published books on the topic. Take control of your organization's big data analytics to produce real results with a resource that is comprehensive in scope and light on hyperbole.
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Data Mining and Big Data

10387) constitutes the proceedings of the Second International Conference on Data Mining and Big Data (DMBD 2017), which was held in conjunction with the 8th International Conference on Swarm Intelligence (ICSI 2017), from July 27 to ...

Author: Ying Tan

Publisher: Springer

ISBN: 9783319618456

Category: Computers

Page: 546

View: 858

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This book constitutes the refereed proceedings of the Second International Conference on Data Mining and Big Data, DMBD 2017, held in Fukuoka, Japan, in July/August 2017. The 53 papers presented in this volume were carefully reviewed and selected from 96 submissions. They were organized in topical sections named: association analysis; clustering; prediction; classification; schedule and sequence analysis; big data; data analysis; data mining; text mining; deep learning; high performance computing; knowledge base and its framework; and fuzzy control.
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Predictive Analytics Data Mining and Big Data

Big data fordummies. Wiley. I found this to be one ofthemore accessible introductions to BigData.Italsoprovides simple descriptions ofthe Hadoop familyof BigData toolsandhowHadoopcanbeappliedto business problems. Silver, N. (2012).

Author: S. Finlay

Publisher: Springer

ISBN: 9781137379283

Category: Business & Economics

Page: 260

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This in-depth guide provides managers with a solid understanding of data and data trends, the opportunities that it can offer to businesses, and the dangers of these technologies. Written in an accessible style, Steven Finlay provides a contextual roadmap for developing solutions that deliver benefits to organizations.
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Big Data Mining and Complexity

This book offers a much needed critical introduction to data mining and ‘big data’.

Author: Brian C. Castellani

Publisher: SAGE

ISBN: 9781529710991

Category: Social Science

Page: 180

View: 225

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This book offers a much needed critical introduction to data mining and ‘big data’. Supported by multiple case studies and examples, the authors provide: Digestible overviews of key terms and concepts relevant to using social media data in quantitative research. A critical review of data mining and ‘big data’ from a complexity science perspective, including its future potential and limitations A practical exploration of the challenges of putting together and managing a ‘big data’ database An evaluation of the core mathematical and conceptual frameworks, grounded in a case-based computational modeling perspective, which form the foundations of all data mining techniques Part of The SAGE Quantitative Research Kit, this book will give you the know-how and confidence needed to succeed on your quantitative research journey.
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Data Mining and Big Data

This book constitutes the refereed proceedings of the Third International Conference on Data Mining and Big Data, DMBD 2018, held in Shanghai, China, in June 2018.

Author: Ying Tan

Publisher: Springer

ISBN: 9783319938035

Category: Computers

Page: 799

View: 284

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This book constitutes the refereed proceedings of the Third International Conference on Data Mining and Big Data, DMBD 2018, held in Shanghai, China, in June 2018. The 74 papers presented in this volume were carefully reviewed and selected from 126 submissions. They are organized in topical sections named: database, data preprocessing, matrix factorization, data analysis, visualization, visibility analysis, clustering, prediction, classification, pattern discovery, text mining and knowledge management, recommendation system in social media, deep learning, big data, Industry 4.0, practical applications
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Proceedings of 4th International Conference on BigData Analysis and Data Mining 2017

4th International Conference on Big Data Analysis and DataMining September 07-08, 2017 | Paris, France Michael Valivullah National Agricultural Statistics Service, USA Big data, data hubs, sensors, IoT and precision agriculture – their ...

Author: ConferenceSeries

Publisher: ConferenceSeries

ISBN:

Category:

Page: 95

View: 471

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September 07-08, 2017 Paris, France Key Topics : Cloud computing, Forecasting from Big Data, Optimization and Big Data, New visualization techniques, Social network analysis, Search and data mining, Complexity and Algorithms, Open Data, ETL (Extract, Transform and Load), OLAP Technologies, Big Data Algorithm, Data Mining Analysis, Kernel Methods, Frequent Pattern Mining, Clustering, Data Privacy and Ethics, Big Data Technologies, Business Analytics, Data Mining Methods and Algorithms, Data Mining Tasks and Processes, Data Mining Applications in Science, Engineering, Healthcare and Medicine, Big Data Applications, Data Mining Tools and Software, Data Warehousing, Artificial Intelligence,
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Integration of Data Mining in Business Intelligence Systems

“Bigdata” Mining With the emergence of “bigdata” as a technology trend in storage and access of data, “bigdata mining”, i.e., mining from bigdata sources is emerging as a related research topic (Che, Safran & Peng, 2013).

Author: Azevedo, Ana

Publisher: IGI Global

ISBN: 9781466664784

Category: Computers

Page: 314

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Uncovering and analyzing data associated with the current business environment is essential in maintaining a competitive edge. As such, making informed decisions based on this data is crucial to managers across industries. Integration of Data Mining in Business Intelligence Systems investigates the incorporation of data mining into business technologies used in the decision making process. Emphasizing cutting-edge research and relevant concepts in data discovery and analysis, this book is a comprehensive reference source for policymakers, academicians, researchers, students, technology developers, and professionals interested in the application of data mining techniques and practices in business information systems.
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Integration Challenges for Analytics Business Intelligence and Data Mining

Comparing to previous data of publication productivity, Liechtenstein and Slovenia have higher citations than their ... This work justifies its interest and importance in the field of Data Mining, Big Data, Business Intelligence, ...

Author: Azevedo, Ana

Publisher: IGI Global

ISBN: 9781799857839

Category: Computers

Page: 250

View: 813

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As technology continues to advance, it is critical for businesses to implement systems that can support the transformation of data into information that is crucial for the success of the company. Without the integration of data (both structured and unstructured) mining in business intelligence systems, invaluable knowledge is lost. However, there are currently many different models and approaches that must be explored to determine the best method of integration. Integration Challenges for Analytics, Business Intelligence, and Data Mining is a relevant academic book that provides empirical research findings on increasing the understanding of using data mining in the context of business intelligence and analytics systems. Covering topics that include big data, artificial intelligence, and decision making, this book is an ideal reference source for professionals working in the areas of data mining, business intelligence, and analytics; data scientists; IT specialists; managers; researchers; academicians; practitioners; and graduate students.
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Big Data Mining and Complexity

Chapter summary • In the world of data mining big data, geospatial modelling plays a crucial role, as it seeks to reveal (i.e. identify, locate, find, uncover) the relationship that geography and geospatial factors have with other key ...

Author: Brian C. Castellani

Publisher: SAGE

ISBN: 9781529711011

Category: Reference

Page: 180

View: 847

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This book offers a much needed critical introduction to data mining and ‘big data’. Supported by multiple case studies and examples, the authors provide everything needed to explore, evaluate and review big data concepts and techniques.
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Metaheuristics for Big Data

This concerns specifically data mining, as it will be defined later, and requires efficient approaches. This book focuses on this level of analysis. In contrast to traditional data, Big Data varies in terms of volume, variety, velocity, ...

Author: Clarisse Dhaenens

Publisher: John Wiley & Sons

ISBN: 9781119347606

Category: Computers

Page: 212

View: 115

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Big Data is a new field, with many technological challenges to be understood in order to use it to its full potential. These challenges arise at all stages of working with Big Data, beginning with data generation and acquisition. The storage and management phase presents two critical challenges: infrastructure, for storage and transportation, and conceptual models. Finally, to extract meaning from Big Data requires complex analysis. Here the authors propose using metaheuristics as a solution to these challenges; they are first able to deal with large size problems and secondly flexible and therefore easily adaptable to different types of data and different contexts. The use of metaheuristics to overcome some of these data mining challenges is introduced and justified in the first part of the book, alongside a specific protocol for the performance evaluation of algorithms. An introduction to metaheuristics follows. The second part of the book details a number of data mining tasks, including clustering, association rules, supervised classification and feature selection, before explaining how metaheuristics can be used to deal with them. This book is designed to be self-contained, so that readers can understand all of the concepts discussed within it, and to provide an overview of recent applications of metaheuristics to knowledge discovery problems in the context of Big Data.
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