A practical, step-by-step approach to making sense out of dataMaking Sense of Data educates readers on the steps and issues that need to be considered in order to successfully complete a data analysis or data mining project. The author provides clear explanations that guide the reader to make timely and accurate decisions from data in almost every field of study. A step-by-step approach aids professionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. With a comprehensive collection of methods from both data analysis and data mining disciplines, this book successfully describes the issues that need to be considered, the steps that need to be taken, and appropriately treats technical topics to accomplish effective decision making from data.Readers are given a solid foundation in the procedures associated with complex data analysis or data mining projects and are provided with concrete discussions of the most universal tasks and technical solutions related to the analysis of data, including:* Problem definitions* Data preparation* Data visualization* Data mining* Statistics* Grouping methods* Predictive modeling* Deployment issues and applicationsThroughout the book, the author examines why these multiple approaches are needed and how these methods will solve different problems. Processes, along with methods, are carefully and meticulously outlined for use in any data analysis or data mining project.From summarizing and interpreting data, to identifying non-trivial facts, patterns, and relationships in the data, to making predictions from the data, Making Sense of Data addresses the many issues that need to be considered as well as the steps that need to be taken to master data analysis and mining.
Download
Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining
Labels: Data Mining
Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques (Massive Computing)
This book will give the reader a perspective into the core theory and practice of data mining and knowledge discovery (DM and KD). Its chapters combine many theoretical foundations for various DM and KD methods, and they present a rich array of examples – many of which are drawn from real-life applications. Most of the theoretical developments discussed are accompanied by an extensive empirical analysis, which should give the reader both a deep theoretical and practical insight into the subjects covered.
The book presents the combined research experiences of its 40 authors gathered during a long search in gleaning new knowledge from data. The last page of each chapter has a brief biographical statement of its contributors, who are world-renowned experts.
45275 KBPDF downloadFilename was cut off by Rapidshare -- Make sure to add the .PDF extension to the downloaded file!
Download
Labels: Data Mining
Grouping Multidimensional Data : Recent Advances in Clustering
Clustering is one of the most fundamental and essential data analysis techniques. Clustering can be used as an independent data mining task to discern intrinsic characteristics of data, or as a preprocessing step with the clustering results then used for classification, correlation analysis, or anomaly detection. Kogan and his co-editors have put together recent advances in clustering large and high-dimension data. Their volume addresses new topics and methods which are central to modern data analysis, with particular emphasis on linear algebra tools, opimization methods and statistical techniques. The contributions, written by leading researchers from both academia and industry, cover theoretical basics as well as application and evaluation of algorithms, and thus provide an excellent state-of-the-art overview. The level of detail, the breadth of coverage, and the comprehensive bibliography make this book a perfect fit for researchers and graduate students in data mining and in many other important related application areas.
Download
Labels: Data Mining
Discovering Knowledge in Data: An Introduction to Data Mining
Learn Data Mining by doing data mining
Data mining can be revolutionary-but only when it's done right. The powerful black box data mining software now available can produce disastrously misleading results unless applied by a skilled and knowledgeable analyst. Discovering Knowledge in Data: An Introduction to Data Mining provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets.
Employing a "white box" methodology and with real-world case studies, this step-by-step guide walks readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets. Principal topics include:
* Data preprocessing and classification
* Exploratory analysis
* Decision trees
* Neural and Kohonen networks
* Hierarchical and k-means clustering
* Association rules
* Model evaluation techniques
Complete with scores of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in Data: An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field the power to turn any data warehouse into actionable knowledge.
An Instructor's Manual presenting detailed solutions to all the problems in the book is available online.
Download
Labels: Data Mining
Data Mining with SQL Server 2005
Your in-depth guide to using the new Microsoft® data mining standard to solve today's business problems Concealed inside your data warehouse and data marts is a wealth of valuable information just waiting to be discovered. All you need are the right tools to extract that information and put it to use.
Serving as your expert guide, this book shows you how to create and implement data mining applications that will find the hidden patterns from your historical datasets. The authors explore the core concepts of data mining as well as the latest trends.
They then reveal the best practices in the field, utilizing the innovative features of SQL Server 2005 so that you can begin building your own successful data mining projects.
You'll learn: The principal concepts of data mining
+ How to work with the data mining algorithms included in SQL Server data mining
+ How to use DMX-the data mining query language The XML for Analysis API The architecture of the SQL Server 2005 data mining component
+ How to extend the SQL Server 2005 data mining platform by plugging in your own algorithms
+ How to implement a data mining project using SQL Server Integration Services
+ How to mine an OLAP cube How to build an online retail site with cross-selling features
+ How to access SQL Server 2005 data mining features programmatically
Download (5.2 MB)
or
Download ebook
or
Download ebook
or
Download ebook
Labels: Data Mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
This is the second edition of the author's Data Mining book. The first part of the book focuses on data mining algorithms, implementation issues, and how to evaluate the results of the data mining model. The second part focuses on the authors "Weka Machine Learning Workbench" which is available under a GNU General Public License. See their web site: http://www.cs.waikato.ac.nz/~ml/weka/index.html for the software. This software appears to be widely used at academic institutions.
The first section of the book provides an overview of the algorithms that the software implements. If you need an in depth understanding of the algorithms, you will need additional information sources. If you simply download the software without an understanding of which algorithms are appropriate to your data mining problem, you may become frustrated with the performance, or, even worse, you may misinterpret the results of the data mining model.
In general, learning data mining is much more complex than this book (or any other single book) can adequately describe; however, this is an excellent source for someone interested in data mining.
Download (5.2 MB)
or
Download ebook
or
Download ebook
Labels: Data Mining
Data Mining, Second Edition : Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)
Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge.
Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data including stream data, sequence data, graph structured data, social network data, and multi-relational data.
Whether you are a seasoned professional or a new student of data mining, this book has much to offer you:
* A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data.
* Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning.
* Dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects.
* Complete classroom support for instructors at www.mkp.com/datamining2e companion site.
Download (27.3 MB)
or
Download ebook
or
Download ebook
Labels: Data Mining
Java Data Mining: Strategy, Standard, and Practice: A Practical Guide for architecture, design, and implementation (The Morgan Kaufmann Series in Data
This is not only a great introduction to JDM, but also a great introduction for a practitioner to data mining in general. This is a must have" for anyone developing large scale data mining applications in Java.
Robert Grossman, Open Data Group and University of Illinois at Chicago
It pleases me that the Java Community Process(sm)(JCPsm) Program could host the development of the Data Mining standard, JSR 73, whose evolution and usability are presented so compellingly in Java Data Mining: Standard, Strategy and Practice. The authors have taken a unique approach to describing a broad range of aspects from strategies to problem solving with data mining technology in a variety of industries. The book is a must-read for those who want to introduce themselves to Java data mining (JDM) and fully realize the strategic importance of this technology in an ever competitive environment.
Onno Kluyt, senior director, JCP Program at Sun Microsystems, Inc. and Chair of the JCP
Java is now ubiquitous, and over the past few years the Java world has shifted focus on--among other things--new frameworks, such as the Java Data Mining (JDM) framework. JDM addresses a clear need for standardization in data mining operations, yet to those approaching both Java and data mining the mountain seems as Everest. Hornick, Marcadé, and Venkayala could not have written this book at a better time. To the expert it is a reference and map of the landscape, and to the novice it will be a constant guide and companion to each journey in JDM. This book is approachable, usable, practical, and necessary for any Java data mining software architect, developer, or analyst.
Frank Byrum, Chief Scientist, CorMine Intelligent Data, LLC
Book Description
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.
The book discusses and illustrates how to solve real problems using the JDM API. The authors provide you with:
* Data mining introductionan overview of data mining and the problems it can address across industries; JDMs place in strategic solutions to data mining-related problems;
* JDM essentialsconcepts, 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 practicethe 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
* Data mining introductionan overview of data mining and the problems it can address across industries; JDM's place in strategic solutions to data mining-related problems;
* JDM essentialsconcepts, 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 practicethe 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
Download Link
Labels: Data Mining
Data Mining books collection
(EBOOK) Data Mining - Bioinformatic.PDF 29-Jan-2007 17:20 512K
0471228524 [Wiley-IEEE Press, 2002] Data Mining - Concepts, Models, Methods, and Algorithms (Paperback) 1.chm 29-Jan-2007 17:20 8.4M
1Tese_IMP_Data_mining_in_medical_databases.pdf 29-Jan-2007 17:20 8.4M
Computer Science - MIT Press - Principles Of Data Mining.pdf 29-Jan-2007 17:20 3.7M
McGrawHill-Machine-Learning-Tom-Mitchell.pdf 29-Jan-2007 17:20 37M
Morgan-Kaufmann-Jiawei-Han-Micheline-Kamber-DataMining-Concepts-and-Techniques.pdf 29-Jan-2007 17:20 3.4M
Morgan.Kaufmann.Data.Mining.Practical.Machine.Learning.Tools.And.Techniques.2nd.ed.2005.pdf 29-Jan-2007 17:20 7.8M
Morgan.Kaufmann.Information.Visualization.Perception2004),.2Ed.pdf 29-Jan-2007 17:20 780K
Wiley - IEEE Press - Data Mining Methods and Models Jan 2006.pdf 29-Jan-2007 17:20 6.2M
recursive-Feature Selection.pdf 29-Jan-2007 17:20 89K
601.66 Febr. 26.doc 23-Feb-2007 10:54 24K
AnalogyLearning.pdf 25-May-2006 14:32 287K
Cluster Analysis.pdf 30-Jan-2007 13:13 1.6M
Comparisons.pdf 09-Feb-2007 08:20 83K
Concept Learning.pdf 02-Feb-2007 08:36 401K
Data Mining.pdf 03-Jul-2006 13:32 405K
Decision.pdf 16-Jan-2007 12:58 264K
Decision Trees.pdf 11-Jul-2006 10:45 296K
Evolutionary Algorit..> 24-Jan-2007 08:30 534K
Examples and Applica..> 17-May-2006 12:43 -
InductiveLogProgr.pdf 22-Dec-2003 23:05 183K
Introduction.pdf 07-Jan-2007 14:08 1.2M
Learning organizatio..> 26-Dec-2003 03:16 186K
MachineLearn ingDesc..> 16-Jan-2007 12:57 33K
Midterm presentation..> 09-Feb-2007 08:20 31K
PAC-Learning.pdf 25-May-2006 14:32 189K
Preprocessing and Vi..> 02-Jan-2006 11:34 335K
Project Hints 06.pdf 22-Jun-2006 08:24 34K
Reinforcement Learni..> 06-Feb-2007 12:56 313K
ReviewFinal06.pdf 18-Jun-2006 17:05 967K
Tools&Evaluation.pdf 02-Jan-2006 16:49 235K
learninginformalobje..> 14-Feb-2006 09:51 605K
neural nets 1.pdf 30-May-2006 08:32 563K
neural nets 2.pdf 03-Jul-2006 13:32 1.0M
index of parent directory
ACA-7-22-2004.pdf 13-Aug-2004 14:18 8.1M
AMDEC-9-2004.pdf 03-Nov-2004 15:36 7.7M
BioGrid04-SnB-Grid-enabled-data-mining-4-2004.pdf 21-May-2004 12:32 2.2M
BioGrid04-SnB-on-Grid-4-2004.pdf 21-May-2004 12:35 5.4M
Bucher-SHARCNET.pdf 03-Nov-2004 14:59 15M
CCGrid04-1.pdf 02-Apr-2004 09:17 641K
CCGrid04-2.pdf 02-Apr-2004 09:17 535K
Cornelius-SHARCNET.pdf 03-Nov-2004 14:59 17M
EAS2003.pdf 02-Apr-2004 09:16 1.0M
Evolutionary-Molecular-Structure-Determination-Data Mining.pdf 03-Nov-2004 15:56 683K
Furlani-SHARCNET.pdf 03-Nov-2004 14:59 28M
GT04-5-2004.pdf 13-Aug-2004 14:18 2.6M
Gallo-SHARCNET.pdf 03-Nov-2004 14:59 27M
Green-Collaboration-SHARCNET.pdf 03-Nov-2004 14:59 19M
Green-Grid-Initiatives-SHARCNET.pdf 03-Nov-2004 14:59 24M
HPDC13-Grid3-final.pdf 13-May-2004 07:15 332K
IBM-6-17-2004.pdf 01-Sep-2004 03:55 4.3M
Marcus-5-2003.pdf 02-Apr-2004 08:58 4.1M
Miller-SHARCNET.pdf 03-Nov-2004 14:59 43M
Molecular-Structure-Determination-Grid.pdf 03-Nov-2004 15:56 900K
Open-Science-Grid-External-Sciences.pdf 09-Sep-2004 12:51 32M
PPL04-1.pdf 02-Apr-2004 09:16 294K
Ruby-SHARCNET.pdf 03-Nov-2004 14:59 19M
SC2003-Panel.pdf 02-Apr-2004 08:57 2.1M
SC2004-Grid-Workshop-8-2004-9-pages.pdf 01-Sep-2004 03:30 814K
SHARCNET-6-24-2004.pdf 13-Aug-2004 14:18 5.1M
SURA-1-2003.pdf 02-Apr-2004 08:58 2.5M
Shah-SHARCNET.pdf 03-Nov-2004 14:59 22M
SnB-DataMining-Grid-PCJ-4-2004.pdf 13-May-2004 07:26 610K
SnB-on-the-Grid-PCJ-4-2004.pdf 13-May-2004 07:26 850K
autonomic-computing-and-grid.pdf 02-Apr-2004 08:44 331K
condor-and-the-grid.pdf 18-Mar-2004 20:32 126K
data-grid-generating-tool.pdf 18-Mar-2004 11:42 2.6M
data-grid-scenario-builder.pdf 18-Mar-2004 11:40 802K
grid-resource-allocation.pdf 18-Mar-2004 11:53 135K
grid3-external-sciences-talk.pdf 10-Sep-2004 08:00 2.2M
hwi-grid-1-2004.pdf 19-Mar-2004 08:03 3.2M
narada-brokering.pdf 18-Mar-2004 11:55 497K
open-grid-services-architecture-and-data-grids.pdf 02-Apr-2004 08:46 258K
osg-ccr-overview.pdf 13-Aug-2004 15:49 3.1M
osg-marklgreen-01-12-04.pdf 13-Aug-2004 15:11 555K
peer-to-peer-grids.pdf 18-Mar-2004 11:34 1.1M
Labels: Data Mining
Data Mining Patterns: New Methods and Application
Since the introduction of the Apriori algorithm a decade ago, the problem of mining patterns is becoming a very active research area, and efficient techniques have been widely applied to the problems either in industry or science. Currently, the data mining community is focusing on new problems such as: mining new kinds of patterns, mining patterns under constraints, considering new kinds of complex data, and real-world applications of these concepts.
Data Mining Patterns: New Methods and Applications provides an overall view of the recent solutions for mining, and also explores new kinds of patterns. This book offers theoretical frameworks and presents challenges and their possible solutions concerning pattern extractions, emphasizing both research techniques and real-world applications. Data Mining Patterns: New Methods and Applications portrays research applications in data models, techniques and methodologies for mining patterns, multi-relational and multidimensional pattern mining, fuzzy data mining, data streaming, incremental mining, and many other topics.
Download
Labels: Data Mining
Data Mining and Knowledge Discovery Technologies (Advances in Data Warehousing and Mining)
As information technology continues to advance in massive increments, the bank of information available from personal, financial, and business electronic transactions and all other electronic documentation and data storage is growing at an exponential rate. With this wealth of information comes the opportunity and necessity to utilize this information to maintain competitive advantage and process information effectively in real-world situations.
Data Mining and Knowledge Discovery Technologies presents researchers and practitioners in fields such as knowledge management, information science, Web engineering, and medical informatics, with comprehensive, innovative research on data mining methods, structures, tools, and methods, the knowledge discovery process, and data marts, among many other cutting-edge topics.
http://www.filefactory.com/file/d4b18e/
http://rapidshare.com/files/97578933/h045.rar
Labels: Data Mining
Emerging Technologies of Text Mining: Techniques and Applications
Massive amounts of textual data make up most organizations stored information. Therefore, there is increasingly high demand for a comprehensive resource providing practical hands-on knowledge for real-world applications.
Emerging Technologies of Text Mining: Techniques and Applications provides the most recent technical information related to the computational models of the text mining process, discussing techniques within the realms of classification, association analysis, information extraction, and clustering. Offering an innovative approach to the utilization of textual information mining to maximize competitive advantage, Emerging Technologies of Text Mining: Techniques and Applications will provide libraries with the defining reference on this topic..
Download
Labels: Data Mining
Mathematical Methods for Knowledge Discovery and Data Mining
The field of data mining has seen a demand in recent years for the development of ideas and results in an integrated structure. Mathematical Methods for Knowledge Discovery & Data Mining focuses on the mathematical models and methods that support most data mining applications and solution techniques, covering such topics as association rules; Bayesian methods; data visualization; kernel methods; neural networks; text, speech, and image recognition; and many others. This Premier Reference Source is an invaluable resource for scholars and practitioners in the fields of biomedicine, engineering, finance and insurance, manufacturing, marketing, performance measurement, and telecommunications.
Download
Labels: Data Mining
Lecture Notes in Data Mining
This book is a series of seventeen edited "student-authored lectures" which explore in depth the core of data mining (classification, clustering and association rules) by offering overviews that include both analysis and insight. The initial chapters lay a framework of data mining techniques by explaining some of the basics such as applications of Bayes Theorem, similarity measures, and decision trees. Before focusing on the pillars of classification, clustering and association rules, the book also considers alternative candidates such as point estimation and genetic algorithms.
download:
http://rapidshare.com/files/76952444/Lecture_Notes_in_Data_Mining_ertu.rar
http://depositfiles.com/files/2731184
Labels: Data Mining