Introduction to Data Mining and Knowledge Discovery, Third Edition" (©1999) is a readable 36-page booklet aimed at business users who want a clear, non-technical overview of the techniques and capabilities of data mining. It’s a valuable educational tool for prospective users. Available as a PDF file, the contents have been bookmarked for your convenience. Introduction...   Read Article.

Data Mining Glossary -Visit the site for more.. accuracy Accuracy is an important factor in assessing the success of data mining. When applied to data, accuracy refers to the rate of correct values in the data. When applied to models, accuracy refers to the degree of fit between the model and the data. This measures how error-free the model's...   Read Article.

Spatial Clustering Methods in Data Mining PDF Whitepaper with 29 pages....   Read Article.

This document is a survey of data mining projects and opportunities throughout the Dun Bradstreet organization. Data mining is a powerful new technology with greater potential to help DB "preemptively define the information market of tomorrow." DB companies already know how to collect and refine massive quantities of data to deliver relevant and...   Read Article.

Data warehousing - the practice of creating huge, central stores of customer data that can be used throughout the enterprise - is becoming more and more commonplace. But data warehouses are useless if companies don't have the proper applications for accessing and using the data. Two popular types of applications that leverage companies' investments...   Read Article.

The purpose of data visualization is to give the user an understanding of what is going on. Since data mining usually involves extracting "hidden" information from a database, this understanding process can get somewhat complicated. Because the user does not know beforehand what the data mining process has discovered, it is a much bigger leap to take...   Read Article.

Data mining is a relatively unique process. In most standard database operations, nearly all of the results presented to the user are something that they knew existed in the database already. Data mining, on the other hand, extracts information from a database that the user did not know existed. Relationships between variables and customer behaviors...   Read Article.

Most marketers understand the value of collecting customer data, but also realize the challenges of leveraging this knowledge to create intelligent, proactive pathways back to the customer. Data mining - technologies and techniques for recognizing and tracking patterns within data - helps businesses sift through layers of seemingly unrelated data for...   Read Article.

This overview provides a description of some of the most common data mining algorithms in use today. We have broken the discussion into two sections, each with a specific theme: 1) Classical Techniques such as statistics, neighborhoods and clustering, and 2) Next Generation Techniques such as trees, networks and rules. Each section will describe a...   Read Article.

The following links point to a set of tutorials on many aspects of statistical data mining, including the foundations of probability, the foundations of statistical data analysis, and most of the classic machine learning and data mining algorithms. These include classification algorithms such as decision trees, neural nets, Bayesian classifiers,...   Read Article.

The amount of electronically available data has grown rapidly because of increase in use of electronic data gathering devices, e.g., point-of-sale, remote sensing devices etc., and because data storage has become easier and cheaper with increasing computing power and disk storage capacity. Data base management systems (DBMSs) have given access to...   Read Article.

You will find here all the material which is relevant for the lab exercises, that is the complete description of the exercises done each time. Information about the used software. In order to have an idea of the material that will be presented in the lab you can take a look at the previous years: Course - 23/10/04, Reminder of basic notions from...   Read Article.

As a result of successfully completing this course students will be able to set up well-defined learning problems, apply effective algorithms to such such problems and use the relevant theory to interpret and evaluate the results. By the end of the subject, students should be able to: • set up a well-defined learning problem for a given task •...   Read Article.

This is white paper talks about An Introduction to Data Mining Discovering hidden value in your data warehouse....   Read Article.