By Thearling K.

This white paper presents an advent to the elemental applied sciences of information mining. Examples of ecocnomic purposes illustrate its relevance to modern day company setting in addition to a uncomplicated description of ways facts warehouse architectures can evolve to convey the worth of knowledge mining to finish clients.

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100 Dose < 160 N N Dose ? 160 Y 61 Supervised Algorithm Summary — kNN — Quick and easy — Models tend to be very large — Neural Networks — Difficult to interpret — Can require significant amounts of time to train — Rule Induction — Understandable — Need to limit calculations — Decision Trees — Understandable — Relatively fast — Easy to translate into SQL queries 62 31 Other Supervised Data Mining Techniques — Support vector machines — Bayesian networks — Naïve Bayes — Genetic algorithms — More of a search technique than a data mining algorithm — Many more...

87 Small Multiples — Coherently present a large amount of information in a small space — Encourage the eye to make comparisons 88 44 PPD Informatics: CrossGraphs 89 OLAP Analysis 90 45 Micro/Macro — Show multiple scales simultaneously 91 Inxight: Table Lens 92 46 Thank You.

Unusual words) — Domain expertise — Linguistic analysis — Example: Cymfony BrandManager — Identify documents ? extract theme ? 5B in 2005 — Depends on what you call “data mining” — Less of a focus towards applications as initially thought — Instead, tool vendors slowly expanding capabilities — Standardization — XML > CWM, PMML, GEML, Clinical Trial Data Model, … — Web services? — Integration — Between applications — Between database & application 70 35 What is Currently Happening in the Marketplace?

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An Introduction to Data Mining by Thearling K.

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New PDF release: An Introduction to Data Mining
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