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Transparent Data Mining For Big And Small Data


Transparent Data Mining For Big And Small Data


File Size : 48,7 Mb
Total Download : 408

Author : Tania Cerquitelli
language : en
Publisher: Springer
Release Date : 2019-05-29



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Visual Data Mining


Visual Data Mining


File Size : 49,6 Mb
Total Download : 778

Author : Simeon Simoff
language : en
Publisher: Springer
Release Date : 2008-07-23



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Visual Data Mining—Opening the Black Box Knowledge discovery holds the promise of insight into large, otherwise opaque datasets. Thenatureofwhatmakesaruleinterestingtoauserhasbeendiscussed 1 widely but most agree that it is a subjective quality based on the practical u- fulness of the information. Being subjective, the user needs to provide feedback to the system and, as is the case for all systems, the sooner the feedback is given the quicker it can in?uence the behavior of the system. There have been some impressive research activities over the past few years but the question to be asked is why is visual data mining only now being - vestigated commercially? Certainly, there have been arguments for visual data 2 mining for a number of years – Ankerst and others argued in 2002 that current (autonomous and opaque) analysis techniques are ine?cient, as they fail to - rectly embed the user in dataset exploration and that a better solution involves the user and algorithm being more tightly coupled. Grinstein stated that the “current state of the art data mining tools are automated, but the perfect data mining tool is interactive and highly participatory,” while Han has suggested that the “data selection and viewing of mining results should be fully inter- tive, the mining process should be more interactive than the current state of the 2 art and embedded applications should be fairly automated . ” A good survey on 3 techniques until 2003 was published by de Oliveira and Levkowitz .

Data Mining In Finance


Data Mining In Finance


File Size : 42,6 Mb
Total Download : 480

Author : Boris Kovalerchuk
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-04-18



Download Data Mining In Finance written by Boris Kovalerchuk and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-04-18 with Computers categories.


Data Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, ruled-based, decision-tree, and fuzzy-logic methods, and then examines the suitability of these approaches to financial data mining. The book focuses specifically on relational data mining (RDM), which is a learning method able to learn more expressive rules than other symbolic approaches. RDM is thus better suited for financial mining, because it is able to make greater use of underlying domain knowledge. Relational data mining also has a better ability to explain the discovered rules - an ability critical for avoiding spurious patterns which inevitably arise when the number of variables examined is very large. The earlier algorithms for relational data mining, also known as inductive logic programming (ILP), suffer from a relative computational inefficiency and have rather limited tools for processing numerical data. Data Mining in Finance introduces a new approach, combining relational data mining with the analysis of statistical significance of discovered rules. This reduces the search space and speeds up the algorithms. The book also presents interactive and fuzzy-logic tools for `mining' the knowledge from the experts, further reducing the search space. Data Mining in Finance contains a number of practical examples of forecasting S&P 500, exchange rates, stock directions, and rating stocks for portfolio, allowing interested readers to start building their own models. This book is an excellent reference for researchers and professionals in the fields of artificial intelligence, machine learning, data mining, knowledge discovery, and applied mathematics.

Data And Applications Security And Privacy Xxxi


Data And Applications Security And Privacy Xxxi


File Size : 50,8 Mb
Total Download : 312

Author : Giovanni Livraga
language : en
Publisher: Springer
Release Date : 2017-06-19



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This book constitutes the refereed proceedings of the 31st Annual IFIP WG 11.3 International Working Conference on Data and Applications Security and Privacy, DBSec 2017, held in Philadelphia, PA, USA, in July 2017. The 21 full papers and 9 short papers presented were carefully reviewed and selected from 59 submissions. The papers are organized in topical sections on access control, privacy, cloud security, secure storage in the cloud, secure systems, and security in networks and Web.

Intelligent Systems And Applications


Intelligent Systems And Applications


File Size : 51,8 Mb
Total Download : 363

Author : Kohei Arai
language : en
Publisher: Springer
Release Date : 2018-11-08



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Gathering the Proceedings of the 2018 Intelligent Systems Conference (IntelliSys 2018), this book offers a remarkable collection of chapters covering a wide range of topics in intelligent systems and computing, and their real-world applications. The Conference attracted a total of 568 submissions from pioneering researchers, scientists, industrial engineers, and students from all around the world. These submissions underwent a double-blind peer review process, after which 194 (including 13 poster papers) were selected to be included in these proceedings. As intelligent systems continue to replace and sometimes outperform human intelligence in decision-making processes, they have made it possible to tackle many problems more effectively. This branching out of computational intelligence in several directions, and the use of intelligent systems in everyday applications, have created the need for such an international conference, which serves as a venue for reporting on cutting-edge innovations and developments. This book collects both theory and application-based chapters on all aspects of artificial intelligence, from classical to intelligent scope. Readers are sure to find the book both interesting and valuable, as it presents state-of-the-art intelligent methods and techniques for solving real-world problems, along with a vision of future research directions.

Data Analytics And Management In Data Intensive Domains


Data Analytics And Management In Data Intensive Domains


File Size : 55,9 Mb
Total Download : 833

Author : Leonid Kalinichenko
language : en
Publisher: Springer
Release Date :



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Mobility Data Management And Exploration


Mobility Data Management And Exploration


File Size : 41,7 Mb
Total Download : 422

Author : Nikos Pelekis
language : en
Publisher: Springer
Release Date : 2014-07-08



Download Mobility Data Management And Exploration written by Nikos Pelekis and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-08 with Computers categories.


This text integrates different mobility data handling processes, from database management to multi-dimensional analysis and mining, into a unified presentation driven by the spectrum of requirements raised by real-world applications. It presents a step-by-step methodology to understand and exploit mobility data: collecting and cleansing data, storage in Moving Object Database (MOD) engines, indexing, processing, analyzing and mining mobility data. Emerging issues, such as semantic and privacy-aware querying and mining as well as distributed data processing, are also covered. Theoretical presentation is smoothly interchanged with hands-on exercises and case studies involving an actual MOD engine. The authors are established experts who address both theoretical and practical dimensions of the field but also present valuable prototype software. The background context, clear explanations and sample exercises make this an ideal textbook for graduate students studying database management, data mining and geographic information systems.