Introduction To XL-Miner

1. Introduction to XLMiner™ The Data mining add-in for Microsoft Excel. XLMiner and Microsoft Office are registered trademarks of the respective owners.

3. Data Utilities – (Sampling)

4. Classification

5. Affinity – (Association Rules)

6. Prediction

7. Time Series Analysis

8. Chartinghttp://dataminingtools.net

9. Introduction to Data Mining Definition: Data mining (or Knowledge Discovery) refers to the process of analyzing a give data set from different precepts and scenarios in order to discover patterns in the given data set Data mining is becoming an increasingly important tool to transform data into information. This information can help reveal the hidden trends about products, customer, market, employees and other factors critical for the success of a company. Data mining It is commonly used in a wide range of profiling practices, such as marketing, surveillance, fraud detection and scientific discovery, machine learning, Biotechnology etc. http://dataminingtools.net

10. Introduction to XLMiner XLMiner™ is a comprehensive data mining add-in for Excel. XLMiner can be used to mine data available in Excel worksheets. It includes capabilities that allow a miner to work with partitioning, neural networks, classification and regression trees, association rules, nearest neighbors, etc. With is ease of use and learning , XLMiner serves to be the perfect candidate tool to wet your feet in Data Mining as a novice miner. http://dataminingtools.net

12. Education edition

13. Professional edition

14. Academic Research editionThe Demo edition and Educational Editions support only Excel. http://dataminingtools.net

15. How to get XLMiner The 30 day demo edition can be downloaded for free from the company website: http://www.resample.com/xlminer/index.shtml System Requirements: Once the download is complete , an installer wizard is opened. Follow the instruction in the wizard, and on completion open Excel and click on the “Add-Ins” tab to use XLMiner or double click on the XLMiner desktop icon. http://dataminingtools.net

16. Overview of the features of XLMiner Partition Data Data Utilities – (Sampling) Classification Affinity – (Association Rules) Prediction Time Series Analysis Data Reduction and exploration. Charting. http://dataminingtools.net

17. Brief description of the features of XLMiner: Partition Data Using this tool we can divide the data sets into mutually exclusive partitions i.e. partitions that do not overlap. Generally we partition the data set into 3 parts- Training Set. Validation set. Test set. We can create the partitions in two ways: Standard Partition: A default 60:40 Training :Validation set ratio partition is set as default, or we may specify it . Oversampling: Used when we want a particular data item which is important but under-represented in the set. http://dataminingtools.net

19. Stratified Sampling.Missing Data handling. Bin Continuous Data. Transform Categorical Data . http://dataminingtools.net

20. Brief description of the features of XLMiner: Classification XLMiner provides a host of classification tools that use efficient classification algorithms to classify data. Discriminant Analysis. Logistic Regression. Classification/Decision tree. Naïve Bayes. Neural network. K-nearest neighbor. http://dataminingtools.net

21. Brief description of the features of XLMiner: Affinity (Association Rules) Affinity or Association rules means finding interesting correlations or associations between different data items in the data set. Usually used for market basket analysis which gives the user a list of product recommendations based on the products he purchases. http://dataminingtools.net

22. Brief description of the features of XLMiner: Prediction XLMiner provides tools that can be used to predict the values of data items in the data sets using different prediction algorithms. Different prediction techniques are: Multiple Linear Regression K-nearest neighbor Regression Tree Neural Network http://dataminingtools.net

23. Brief description of the features of XLMiner: Time Series Time series analysis is done to understand the distribution of data points over time and such an analysis is useful for the purpose of prediction – for example, the future trends over time can be predicted by information extracted from the past performance Exploratory techniques are: ACF (Auto correlation function) PACF (Partial auto correlation function) Soothing and Forecasting http://dataminingtools.net

24. Brief description of the features of XLMiner: Data reduction and Exploration Data exploration is an approach to analyze data for the purpose of formulating hypothesis that can be worth testing. XLMiner provides different tools for this purpose Principal Component Analysis K-Means Clustering Hierarchical Clustering http://dataminingtools.net

25. Brief description of the features of XLMiner: Charts XLMiner provides a charting feature too to chalk up graphs and charts for a more visual and convenient representation of data. Different chart tools are: Histogram. Box Plot. Matrix Plot. http://dataminingtools.net

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