Design and Implementation of Data Mining Tools
Author | : |
Publisher | : |
Total Pages | : |
Release | : 2009 |
Genre | : Data mining |
ISBN | : |
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Author | : |
Publisher | : |
Total Pages | : |
Release | : 2009 |
Genre | : Data mining |
ISBN | : |
Author | : Bhavani Thuraisingham |
Publisher | : CRC Press |
Total Pages | : 272 |
Release | : 2009-06-18 |
Genre | : Computers |
ISBN | : 1420045911 |
Focusing on three applications of data mining, Design and Implementation of Data Mining Tools explains how to create and employ systems and tools for intrusion detection, Web page surfing prediction, and image classification. Mainly based on the authors' own research work, the book takes a practical approach to the subject.The first part of the boo
Author | : |
Publisher | : |
Total Pages | : 250 |
Release | : 2009 |
Genre | : Data mining |
ISBN | : 9780429116704 |
Focusing on three applications of data mining, Design and Implementation of Data Mining Tools explains how to create and employ systems and tools for intrusion detection, Web page surfing prediction, and image classification. Mainly based on the authors' own research work, the book takes a practical approach to the subject. The first part of the book reviews data mining techniques, such as artificial neural networks and support vector machines, as well as data mining applications. The second section covers the design and implementation of data mining tools for intrusion detection. It examines v.
Author | : Suman Lohani |
Publisher | : |
Total Pages | : |
Release | : 2003 |
Genre | : Computer software |
ISBN | : |
Author | : Pragati Manandhar |
Publisher | : |
Total Pages | : 146 |
Release | : 2011 |
Genre | : Data mining |
ISBN | : |
Author | : Mehedy Masud |
Publisher | : CRC Press |
Total Pages | : 450 |
Release | : 2016-04-19 |
Genre | : Computers |
ISBN | : 1439854556 |
Although the use of data mining for security and malware detection is quickly on the rise, most books on the subject provide high-level theoretical discussions to the near exclusion of the practical aspects. Breaking the mold, Data Mining Tools for Malware Detection provides a step-by-step breakdown of how to develop data mining tools for malware d
Author | : D. Braha |
Publisher | : Springer Science & Business Media |
Total Pages | : 530 |
Release | : 2013-03-14 |
Genre | : Computers |
ISBN | : 1475749112 |
Data Mining for Design and Manufacturing: Methods and Applications is the first book that brings together research and applications for data mining within design and manufacturing. The aim of the book is 1) to clarify the integration of data mining in engineering design and manufacturing, 2) to present a wide range of domains to which data mining can be applied, 3) to demonstrate the essential need for symbiotic collaboration of expertise in design and manufacturing, data mining, and information technology, and 4) to illustrate how to overcome central problems in design and manufacturing environments. The book also presents formal tools required to extract valuable information from design and manufacturing data, and facilitates interdisciplinary problem solving for enhanced decision making. Audience: The book is aimed at both academic and practising audiences. It can serve as a reference or textbook for senior or graduate level students in Engineering, Computer, and Management Sciences who are interested in data mining technologies. The book will be useful for practitioners interested in utilizing data mining techniques in design and manufacturing as well as for computer software developers engaged in developing data mining tools.
Author | : Mark F. Hornick |
Publisher | : Elsevier |
Total Pages | : 545 |
Release | : 2010-07-26 |
Genre | : Computers |
ISBN | : 0080495915 |
Whether you are a software developer, systems architect, data analyst, or business analyst, if you want to take advantage of data mining in the development of advanced analytic applications, Java Data Mining, JDM, the new standard now implemented in core DBMS and data mining/analysis software, is a key solution component. This book is the essential guide to the usage of the JDM standard interface, written by contributors to the JDM standard. Data mining introduction - an overview of data mining and the problems it can address across industries; JDM's place in strategic solutions to data mining-related problems JDM essentials - concepts, design approach and design issues, with detailed code examples in Java; a Web Services interface to enable JDM functionality in an SOA environment; and illustration of JDM XML Schema for JDM objects JDM in practice - the use of JDM from vendor implementations and approaches to customer applications, integration, and usage; impact of data mining on IT infrastructure; a how-to guide for building applications that use the JDM API Free, downloadable KJDM source code referenced in the book available here
Author | : Ian H. Witten |
Publisher | : Elsevier |
Total Pages | : 665 |
Release | : 2011-02-03 |
Genre | : Computers |
ISBN | : 0080890369 |
Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise. Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization
Author | : Grace C. Shiao |
Publisher | : |
Total Pages | : 182 |
Release | : 2006 |
Genre | : Data mining |
ISBN | : |
"This thesis describes the design and implementation of the data analysis components. Many features of modern database systems facilitate the decision-making process. Recently, Online Analytical Processing (OLAP) and data mining are increasingly being used in a wide range of applications. OLAP allows users to analyze data from a wide variety of viewpoints. Data mining is the process of selecting, exploring, and modeling large amounts of data to discover previously unknown patterns for business advantage. Microsoft SQL server 2000 Analysis Services provides a rich set of tools to create and to maintain OLAP and data mining objects. In order to use these tools, users need to fully understand the underlying architectures and the specialized technological terms, which are not related to the data analysis. The complexities in the development challenges prevent the data analysts to use these tools effectively. In this work, we developed several components, which can be used as the foundation in the analytical applications. Using these components in the software applications can hide the technical complexities and can provide tools to build the OLAP and mining model and to access data information from these model systems. Developers can also reuse these components without coding from scratch. The reusability of these components enhances the application's reliability and reduces the development costs and time."--abstract.