SEGMENTACION de MERCADOS con Redes Neuronales, Cluster y Mineria de Datos

SEGMENTACION de MERCADOS con Redes Neuronales, Cluster y Mineria de Datos

Author: Jesus Prieto

Publisher: Createspace Independent Pub

Published: 2013-02-13

Total Pages: 104

ISBN-13: 9781482539943

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La clasificación de las técnicas de segmentación de mercados distingue entre técnicas predictivas, en las que las variables que intervienen en el proceso pueden clasificarse inicialmente en dependientes e independientes (similares a las técnicas del análisis de la dependencia o métodos explicativos del análisis multivariante) y técnicas descriptivas, en las que todas las variables tienen inicialmente el mismo estatus (similares a las técnicas del análisis de la interdependencia o métodos descriptivos del análisis multivariante). Las técnicas predictivas de segmentación especifican el modelo para los datos en base a un conocimiento teórico previo. El modelo supuesto para los datos debe contrastarse después del proceso de minería de datos antes de aceptarlo como válido. En algunos casos, el modelo se obtiene como mezcla del conocimiento obtenido antes y después de la segmentación y también debe contrastarse antes de aceptarse como válido. Por ejemplo, las redes neuronales permiten descubrir modelos complejos y afinarlos a medida que progresa la exploración de los datos. Gracias a su capacidad de aprendizaje, permiten descubrir relaciones complejas entre variables sin ninguna intervención externa. Podemos incluir entre estas técnicas todas las técnicas de segmentación en las que subyace un modelo (modelos de elección discreta, análisis discriminante, árboles de decisión, redes neuronales, análisis conjunto, etc.) Estas técnicas también se denominan técnicas de clasificación ya que permiten extraer perfiles de comportamiento o clases, siendo el objetivo construir un modelo que permita clasificar cualquier nuevo dato en una de las clases.En las técnicas descriptivas no se asigna ningún papel predeterminado a las variables. No se supone la existencia de variables dependientes ni independientes y tampoco se supone la existencia de un modelo previo para los datos. Los modelos se crean automáticamente partiendo del reconocimiento de patrones. En este grupo se incluyen las técnicas de clustering y las técnicas de reducción de la dimensión (escalamiento multidimensonal, correspondencias, etc.) En este libro se tratan las técnicas de segmentación de mercados basadas en redes neuronales, análisis clúster y Minería de Datos


RETRACTED BOOK: 151 Trading Strategies

RETRACTED BOOK: 151 Trading Strategies

Author: Zura Kakushadze

Publisher: Springer

Published: 2018-12-13

Total Pages: 480

ISBN-13: 3030027929

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The book provides detailed descriptions, including more than 550 mathematical formulas, for more than 150 trading strategies across a host of asset classes and trading styles. These include stocks, options, fixed income, futures, ETFs, indexes, commodities, foreign exchange, convertibles, structured assets, volatility, real estate, distressed assets, cash, cryptocurrencies, weather, energy, inflation, global macro, infrastructure, and tax arbitrage. Some strategies are based on machine learning algorithms such as artificial neural networks, Bayes, and k-nearest neighbors. The book also includes source code for illustrating out-of-sample backtesting, around 2,000 bibliographic references, and more than 900 glossary, acronym and math definitions. The presentation is intended to be descriptive and pedagogical and of particular interest to finance practitioners, traders, researchers, academics, and business school and finance program students.


Cloud Computing, Big Data & Emerging Topics

Cloud Computing, Big Data & Emerging Topics

Author: Marcelo Naiouf

Publisher: Springer

Published: 2021-08-17

Total Pages: 203

ISBN-13: 9783030848248

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This book constitutes the revised selected papers of the 9th International Conference on Cloud Computing, Big Data & Emerging Topics, JCC-BD&ET 2021, held in La Plata, Argentina*, in June 2021. The 12 full papers and 2 short papers presented were carefully reviewed and selected from a total of 37 submissions. The papers are organized in topical sections on parallel and distributed computing; machine and deep learning; big data; web and mobile computing; visualization.. *The conference was held virtually due to the COVID-19 pandemic.


Machine Learning

Machine Learning

Author:

Publisher: BoD – Books on Demand

Published: 2021-12-22

Total Pages: 153

ISBN-13: 183969484X

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Recent times are witnessing rapid development in machine learning algorithm systems, especially in reinforcement learning, natural language processing, computer and robot vision, image processing, speech, and emotional processing and understanding. In tune with the increasing importance and relevance of machine learning models, algorithms, and their applications, and with the emergence of more innovative uses–cases of deep learning and artificial intelligence, the current volume presents a few innovative research works and their applications in real-world, such as stock trading, medical and healthcare systems, and software automation. The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed. The volume will be useful for advanced graduate and doctoral students, researchers, faculty members of universities, practicing data scientists and data engineers, professionals, and consultants working on the broad areas of machine learning, deep learning, and artificial intelligence.


INTRODUCTION TO DATA MINING WITH CASE STUDIES

INTRODUCTION TO DATA MINING WITH CASE STUDIES

Author: G. K. GUPTA

Publisher: PHI Learning Pvt. Ltd.

Published: 2014-06-28

Total Pages: 537

ISBN-13: 8120350022

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The field of data mining provides techniques for automated discovery of valuable information from the accumulated data of computerized operations of enterprises. This book offers a clear and comprehensive introduction to both data mining theory and practice. It is written primarily as a textbook for the students of computer science, management, computer applications, and information technology. The book ensures that the students learn the major data mining techniques even if they do not have a strong mathematical background. The techniques include data pre-processing, association rule mining, supervised classification, cluster analysis, web data mining, search engine query mining, data warehousing and OLAP. To enhance the understanding of the concepts introduced, and to show how the techniques described in the book are used in practice, each chapter is followed by one or two case studies that have been published in scholarly journals. Most case studies deal with real business problems (for example, marketing, e-commerce, CRM). Studying the case studies provides the reader with a greater insight into the data mining techniques. The book also provides many examples, review questions, multiple choice questions, chapter-end exercises and a good list of references and Web resources especially those which are easy to understand and useful for students. A number of class projects have also been included.


Measuring Scholarly Impact

Measuring Scholarly Impact

Author: Ying Ding

Publisher: Springer

Published: 2014-11-06

Total Pages: 351

ISBN-13: 3319103776

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This book is an authoritative handbook of current topics, technologies and methodological approaches that may be used for the study of scholarly impact. The included methods cover a range of fields such as statistical sciences, scientific visualization, network analysis, text mining, and information retrieval. The techniques and tools enable researchers to investigate metric phenomena and to assess scholarly impact in new ways. Each chapter offers an introduction to the selected topic and outlines how the topic, technology or methodological approach may be applied to metrics-related research. Comprehensive and up-to-date, Measuring Scholarly Impact: Methods and Practice is designed for researchers and scholars interested in informetrics, scientometrics, and text mining. The hands-on perspective is also beneficial to advanced-level students in fields from computer science and statistics to information science.


ICGR 2018 - Proceedings of the International Conference on Gender Research

ICGR 2018 - Proceedings of the International Conference on Gender Research

Author: Ana Azevedo

Publisher: Acpil

Published: 2018-03-22

Total Pages: 650

ISBN-13: 9781911218777

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These proceedings represent the work of researchers participating in the International Conference on Gender Research (ICGR 2018) which is being hosted this year by the ISCAP in Porto, Portugal on 12-13 April 2018. ICGR is a new event on the international research conferences calendar and provides a valuable platform for individuals to present their research findings, display their work in progress and discuss conceptual and empirical advances in the areas surrounding Gender Research. It provides an important opportunity for researchers across a diverse range of fields all looking at aspects relating to Gender to come together with peers to share their varied and valuable experiences. The first day will be opened with a keynote presentation by Bruce I Newman from DePaul University in Chicago, USA who will address the topic Gender and Democracy. In the afternoon, there will be an additional keynote address on Empowering women in the IT/IS research: the importance of role models given by Isabel Ramos from, University of Minho, Portugal. The second day of the conference will be opened by Paola Paoloni from "NiccolÒ Cusano" University, Rome, Italy. Paola will be talking about A Relational Capital Dimension in Universities. In this event, participants will have the opportunity to have access to the latest research and developments concerning Gender Research and after an initial submission of 180 Abstracts, there will be 62 Research Papers, 8 PhD Research Papers, 2 Masters Papers, 1 Non-Academic and 4 Work in Progress Paper published in these Conference Proceedings. These papers represent truly global research in the field, with contributions from Australia, Belgium, Brazil, Canada, Colombia, Costa Rica, Cyprus, Czech Republic, Denmark, France, Germany, Greece, Iran, Italy, Kazakhstan, Lithuania, Malaysia, Mexico, Nepal, Nigeria, Pakistan, Philippines, Poland, Portugal, Russia, Slovakia, South Africa, Spain, Sweden, Taiwan, Thailand, The Netherlands, Turkey, UAE, UK and USA.


Aplicación de un modelo de red neuronal no supervisado a la clasificación de consumidores eléctricos

Aplicación de un modelo de red neuronal no supervisado a la clasificación de consumidores eléctricos

Author: Sergio Valero Verdú

Publisher: Editorial Club Universitario

Published: 2013-01-31

Total Pages: 166

ISBN-13: 8415787065

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El libro muestra la capacidad de las redes neuronales y en concreto de los mapas auto-organizados de Teuvo Kohonen, los conocidos como Self-Organizing Maps (SOM) para clasificar consumidores eléctricos a partir de históricos de datos reales de consumo. El espectro de datos de entrada está formado por más de 20 tipos de consumidores distintos de una misma región geográfica. La red neuronal SOM ha demostrado ser una eficaz herramienta para segmentar y clasificar consumidores a partir de sus perfiles de carga diarios y ha permitido identificar nuevos consumidores, no utilizados antes para entrenar el mapa. Esta identificación posterior y la asignación automática a un segmento o clúster de clientes permiten asociar nuevos consumidores a patrones de consumo previamente clasificados. Este procedimiento permitiría a compañías comercializadoras y a clientes conocer a partir de los datos de consumo diario a qué cluster de consumidores pertenece y elegir tarifas específicas en función del patrón de consumo de este grupo.


Visualizing the Structure of Science

Visualizing the Structure of Science

Author: Benjamín Vargas-Quesada

Publisher: Springer Science & Business Media

Published: 2007-05-19

Total Pages: 311

ISBN-13: 3540697284

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This book presents a methodology for visualizing large scientific domains. Authors Moya-Anegón and Vargas-Queseda create science maps, so-called "scientograms", based on the interactions between authors and their papers through citations and co-citations, using approaches such as domain analysis, social networks, cluster analysis and pathfinder networks. The resulting scientograms offer manifold possibilities.


Marketing and Smart Technologies

Marketing and Smart Technologies

Author: Álvaro Rocha

Publisher: Springer Nature

Published: 2021-03-09

Total Pages: 783

ISBN-13: 9813341831

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This book includes selected papers presented at the International Conference on Marketing and Technologies (ICMarkTech 2020), held at ISCTE - University Institute of Lisbon, in the city of Lisbon in Portugal, between 8 and 10 October 2020. It covers up-to-date cutting-edge research on artificial intelligence applied in marketing, virtual and augmented reality in marketing, business intelligence databases and marketing, data mining and big data, marketing data science, web marketing, e-commerce and v-commerce, social media and networking, geomarketing and IoT, marketing automation and inbound marketing, machine learning applied to marketing, customer data management and CRM, and neuromarketing technologies.