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Learn Data Mining by doing data miningData 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

Data mining --- Data mining. --- Computer Science --- Engineering & Applied Sciences --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching

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This book describes exciting new opportunities for utilizing robust graph representations of data with common machine learning algorithms. Graphs can model additional information which is often not present in commonly used data representations, such as vectors.

Data mining --- Graph theory --- Algorithms --- Multidimensional scaling --- Data processing --- Information systems --- Discrete mathematics --- Data mining. --- Algorithms. --- Multidimensional scaling. --- Psychometrics --- Scaling (Social sciences) --- Algorism --- Algebra --- Arithmetic --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Data processing. --- Foundations --- Graph theory - Data processing

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As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more. Algorithmic methods at the heart of successful data mining including tried and true techniques as well as leading edge methods. Performance improvement techniques that work by transforming the input or output. Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization in a new, interactive interface.

Data mining. --- Database searching. --- Data base searching --- Database search strategies --- Search strategies in databases --- Searching databases --- Electronic information resource searching --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching

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Datamining and Diagnosing Integrated Circuit Fails addresses the problem of obtaining maximum information from (functional) Integrated Circuit fail data about the defects that caused the fails. It starts at the highest level from mere sort codes, and drills down via various data mining techniques to detailed logic diagnosis. The various approaches discussed in this book have a thorough theoretical underpinning, but are geared towards applications on real life fail data and state of the art ICs. This book brings together a large number of analysis techniques that are suitable for IC fail data, but that are not available elsewhere in a single place. Several of the techniques, in fact, have been presented only recently in technical conferences. Datamining and Diagnosing Integrated Circuit Fails begins with a discussion of sort codes and yield analysis. It then discusses various data mining techniques centered on fail syndrome commonalities and the statistics of embedded object fails. It gives a thorough discussion of the area dependence of the yield and of the recognition of spatial patterns of failing die or embedded objects. Next, it gives a detailed analysis of the relationship between defect coverage and yield. It ends with a description of state of the art logic diagnosis techniques. The purpose of the book is to bring together in one place a large number of analysis, data mining and diagnosis techniques that have proven to be useful in analyzing IC fails. The descriptions of the techniques and analysis routines is sufficiently detailed that profession manufacturing engineers can implement them in their own work environment. There are many techniques for analyzing IC fails, but they are scattered over the professional IC test and diagnosis literature, and in various statistics and data mining handbooks. Moreover, many data mining techniques that are standard in other data analysis environments, and that are appropriate for analyzing IC fails, have not yet been employed for that purpose. There is a clear need for a single source for all these analysis techniques, suitable for professional IC manufacturing and test engineers.

Integrated circuits --- Semiconductors --- Data mining. --- Testing --- Statistical methods. --- Failures. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Failures in semiconductors --- System failures (Engineering) --- Chips (Electronics) --- Circuits, Integrated --- Computer chips --- Microchips --- Electronic circuits --- Microelectronics --- Systems engineering. --- Electronics. --- Circuits and Systems. --- Electronics and Microelectronics, Instrumentation. --- Electrical engineering --- Physical sciences --- Engineering systems --- System engineering --- Engineering --- Industrial engineering --- System analysis --- Design and construction --- Electronic circuits. --- Microelectronics. --- Microminiature electronic equipment --- Microminiaturization (Electronics) --- Electronics --- Microtechnology --- Miniature electronic equipment --- Electron-tube circuits --- Electric circuits --- Electron tubes

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Data mining --- Cluster analysis --- 681.3*H2 --- datamining --- zoekstrategieën --- clusteranalyse --- informatica --- 527.3 --- 527.9 --- Database management: security; integrity; protection--See also {?681.5*E5} --- databases - software --- informatica, informatietechnologie - overige specifieke toepassingen --- Cluster analysis. --- Data mining. --- Data recovery (Computer science) --- Data recovery (Computer science). --- 681.3*H2 Database management: security; integrity; protection--See also {?681.5*E5} --- Data reconstruction (Computer science) --- Reconstruction, Data (Computer science) --- Recovery, Data (Computer science) --- Electronic data processing --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Correlation (Statistics) --- Multivariate analysis --- Spatial analysis (Statistics) --- Acqui 2006