Archive for the ‘data mining’ Category

Data Mining, Introduction of Beverage Manufacturing Data – Session 23

January 15th, 2012 No comments

This session introduces a data set that will be used to explore the use of data mining tool for the regression problem. The data, beverage manufacturing, has a continuous target variable. The goal of the data mining project is to determine what variables are good predictors of this continuous variable and to train data mining models to predict it. This session reviews the data mining project goals, reviews the data using basic statistics and graphs, and selects appropriate variables.

Duration : 0:9:58

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Manage the Data Deluge with Data Mining and Predictive Analytics

January 6th, 2012 No comments

Learn how data mining can be applied to identify trends, patterns and relationships while predictive analytics can be used to predict future outcomes.

To learn more about data mining and predictive analytics, visit .

Duration : 0:2:28

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Data Mining, C&RT for Regression – Session 24

December 29th, 2011 No comments

Previously in the series, C&RT and other tree algorithms were discussed for the classification problem. This session uses the regression data, beverage manufacturing, to explore C&RT as well as the other tree algorithms. The options and parameters are reviewed as well as important output. An example analysis is performed in STATISTICA using C&RT. Then the Data Mining Workspace is used to very briefly show the remaining tree tools offered, CHAID, Boosted Trees and Random Forests.

Duration : 0:9:3

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Machine Learning (Introduction + Data Mining VS ML)

December 21st, 2011 No comments

lemiffelearning learning, introduction, data mining, VS machine learningMachine Learning (Introduction + Data Mining VS ML)

Duration : 0:8:31

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Creating Data Mining Structures & Predictive Models using the Excel Add-In for SQL Server 2008

December 13th, 2011 No comments

A demonstration of how to create Data Mining Structures & Predictive Models using the Excel Data mining Addin for SQL Server 2008. A data mining structure is created first and then a Microsoft Decision Tree & Neural Network are created. In the subsequent video I will create a lift chart (also known as an Accuracy Chart) to compare the effectiveness of the two models. The raw data used in the demonstration is available at

Duration : 0:12:11

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Data Mining, Comparing Model Performance – Session 21

December 5th, 2011 1 comment

With the Credit Scoring data, Data Mining models have been built including Classification and Regression Trees, CHAID trees, Random Forest and Boosted Trees. This session will look at STATISTICA tools to deploy these models and compare the results. Lift and Gains charts are used to visually gauge model performance. The Rapid Deployment tool of STATISTICA can be used to deploy many other statistical and data mining models as well.

Duration : 0:7:44

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Data Mining, Random Forest Tools – Session 20

November 28th, 2011 No comments

Session 20 of the Data mining series covers the Random Forest tool. Random Forest build a series of classification trees, each of which is able to classify the target variable. The Random Forest classification is based on the collection of classifications from the individual trees. Performance gains come from using this network of classifiers. This session discusses Random Forest in general, then uses the credit scoring data to give an example of the analysis and results.

Duration : 0:6:43

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Social Networks in Data Mining – Keynote Bart Baesens

November 19th, 2011 No comments

Brooke Fortson interviews Bart Baesens about his keynote address at Analytics 2011. Baesens discusses social networks are being incorporated into analytical models.

To learn more about Analytics 2011, visit .

Duration : 0:3:35

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Data Mining

November 11th, 2011 No comments

Technology students give presentation on about Data Mining including the advantages/disadvantages, how to and more.

Duration : 0:21:40

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Introduction to Data Mining (2/3)

November 3rd, 2011 4 comments This video gives a brief demo of the various data mining techniques. The demo mainly uses SQL server 2008, BIDS 2008 and Excel for data mining

Duration : 0:13:0

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