Implementation of algorithms using WEKA
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PCA is a technique that reduces the dimensionality of a dataset by identifying influential parameters, making predictive modeling and interpreting results easier. The principal components are uncorrelated, cumulatively explain large data variance, and original variables with low weight in components can be removed. Variance measures data spread while covariance is the mean of variable deviations from their means.
Implementation of algorithms using WEKA
- 1. Principal Component Analysis PCA is a technique which will allow reducing the dimension of a dataset by identifying a few most influential parameters (if they exist). This sort of variable screening or feature selection will make it easy to apply other predictive modeling techniques and also make the job of interpreting the results easier. Properties of PCs: a. They are uncorrelated with each other b. They cumulatively contain/explain a large amount of variance within the data c. Original variables with very low weightage factors in their principal components can be removed from the dataset. Variance measures (always non negative) how far a set of numbers is spread out. Covariance is the mean value of the product of the deviations of two variates from their respective means.
- 2. Association http://www.slideshare.net/zafarjcp/data-mining-association-rules-basics
- 3. Comparison of Weka and RapidMiner Both are OpenSource & written in JAVA. RapidMiner Weka More Flexible Easier to use; apt for beginners More Analysis steps Faster Better visualization & suggest fixes More algorithms supported Disadvantages Lot of ETL modules but difficult to use Poor connectivity-non JAVA base DB