David Nettleton
David F. Nettleton is Senior Data Mining Analyst at IRIS Technology Solutions. He also collaborates with the Web Science and Social Computing Research Group of the DTIC at the Pompeu Fabra University in Catalunya, Spain. From 1985 until 2004 he worked for a diversity of companies in different sectors, such as Systems Designers, Plc. (UK), IBM Global Services, Carburos Metalicos, Laboratorios Menarini and Coritel. He has also been involved in business startups, such as TAD Sistemas (acquired by Bertelsmann AG in 2000). Since 2004 he has taught and conducted research at the Pompeu Fabra University (Web Research Group, http://grupoweb.upf.es), the IIIA-CSIC (Ares Team for Advanced Research on Information Security and Privacy, http://www.iiia.csic.es/en/project/ares), the Ramon Llull University with the GRSI (Intelligent Systems Research Group, http://www.salleurl.edu/GRSI/) and IRIS (http://www.iristechnologygroup.com/).
His research interests include industrial data analysis and modeling, machine learning, artificial intelligence and online social network analysis.
Address: Carretera d’Esplugues 39-41
His research interests include industrial data analysis and modeling, machine learning, artificial intelligence and online social network analysis.
Address: Carretera d’Esplugues 39-41
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Papers by David Nettleton
- Illustrates cost-benefit evaluation of potential projects
- Includes vendor-agnostic advice on what to look for in off-the-shelf solutions as well as tips on building your own data mining tools
- Approachable reference can be read from cover to cover by readers of all experience levels
- Includes practical examples and case studies as well as actionable business insights from author's own experience
Description
Whether you are brand new to data mining or working on your tenth predictive analytics project, Commercial Data Mining will be there for you as an accessible reference outlining the entire process and related themes. In this book, you'll learn that your organization does not need a huge volume of data or a Fortune 500 budget to generate business using existing information assets. Expert author David Nettleton guides you through the process from beginning to end and covers everything from business objectives to data sources, and selection to analysis and predictive modeling.
Commercial Data Mining includes case studies and practical examples from Nettleton's more than 20 years of commercial experience. Real-world cases covering customer loyalty, cross-selling, and audience prediction in industries including insurance, banking, and media illustrate the concepts and techniques explained throughout the book.
Readership
Data mining professionals in business & IT.
INDICE RESUMIDO: Introducción. Conceptos y técnicas. La perspectiva difusa. El diagnóstico y el pronóstico clínico. El diagnóstico del síndrome de apnea del sueña. La representación, comparación y proceso de datos de diferentes tipos. Técnicas. Resumen de los aspectos claves en la adaptación e implementación de las técnicas. Aplicación de las técnicas a casos reales. Pronóstico de pacientes de la UCI-Hospital Parc Tauli de Sabadell, etc.,
Prácticamente todos los métodos, técnicas e ideas que se presentan, por ejemplo 'calidad de datos', 'data mart', 'CRM - gestión de la relación con los clientes', 'diferentes fuentes de datos' y 'búsqueda en Internet', pueden ser aprovechados tanto por el empresario de una micro-empresa o un profesional autónomo, como por una empresa mediana o grande. No es imprescindible disponer de un gran volumen de datos, y hay herramientas de análisis disponibles a un precio accesible a todos.
(i) A personalized privacy tool for online social network users
and (ii) a generator for synthetic online social network graph data.
In the following we present an approach for generating a graph topology and populating it with synthetic data for an online social network.
Please reference the paper [1] when using this data and publishing results in your work. Please give me your feedback on your analysis/use of this data and suggestions for improvement.
[1] Nettleton, DF (2015) Generating synthetic online social network graph data and topologies, 3rd Workshop on Graph-based Technologies and Applications (Graph-TA), UPC, Barcelona, Spain, March 18th 2015.
machine learning algorithms together with a “white-box” rule induction technique to create a supervised model of the fitting error between the expected and real force measures. The final objective is to build a precise model of the winding process
in order to control de tension of the material being wound in the first case, and the friction of the material passing through the die, in the second case.
https://github.com/dnettlet/MEDICI
The main project folder includes the corresponding paper (please reference if you include Medici in you research) and user manual.
The paper preprint reference is: https://arxiv.org/abs/2101.01956
Overview:
The Java and JavaFx source code corresponds to the Medici application, designed to produce synthetic data for social network graphs, which can be used for analysis, hypothesis testing and application development by researchers and practitioners in the field. It builds on previous work by providing an integrated system, and a user friendly screen interface. It can be run with default values to produce graph data and statistics, which can then be used for further processing. The system is made publicly available in a Github Java project. The annex provides a user manual with a screen by screen guide.
Language: Repast (ReLogo)
Repository: https://github.com/dnettlet/AgentSim1
License: GNU GENERAL PUBLIC LICENSE Version 3
Languages: Python
Repository: https://github.com/dnettlet/memes
License: GNU GENERAL PUBLIC LICENSE Version 3
"A synthetic data generator for online social network graphs",
Social Network Analysis and Mining, Dec. 2016, 6:44
and the github code ref when you use/adapt/improve it !
https://github.com/dnettlet/SynthOSNdataGenerator
This version with no overlapping communities :)