Join Barton Poulson for an in-depth discussion in this video Data reduction in RapidMiner, part of Data Science Foundations: Data Mining.
- [Narrator] We'll finish our presentation…of Data Reduction,…by looking at the drag and drop application…in RapidMiner.…RapidMiner's a very popular program,…and there are several,…very expensive commercial versions,…but there's also a free community version.…Now, as of Version seven point two,…there's an important limitation.…The community version can only handle 10,000 rows of data.…That may sound like a lot,…but the Big Five data we've been using…has almost 19,000 rows,…so I've had to create a sample data set…that fits into that restriction.…
That maybe a deal breaker for some people with RapidMiner,…but I still wanna show you how it works,…and what you can get from it.…Now if you want to,…you can open up the RapidMiner file I've created.…It's dot RMP for RapidMiner process,…that means the programming.…But I'm going to create this one from scratch,…so you can see how it works from step-to-step.…When you first open the RapidMiner program,…you have a very busy collection of windows and boxes.…You should know however,…
Barton Poulson covers data sources and types, the languages and software used in data mining (including R and Python), and specific task-based lessons that help you practice the most common data-mining techniques: text mining, data clustering, association analysis, and more. This course is an absolute necessity for those interested in joining the data science workforce, and for those who need to obtain more experience in data mining.
- Prerequisites for data mining
- Data mining using R, Python, Orange, and RapidMiner
- Data reduction
- Data clustering
- Anomaly detection
- Association analysis
- Regression analysis
- Sequence mining
- Text mining
Skill Level Beginner
Transitioning from Data Warehousing to Big Datawith Alan Simon1h 50m Intermediate
Big Data Foundations: Program Managementwith Alan Simon1h 11m Intermediate
2. Data Reduction
5. Anomaly Detection
6. Association Analysis
7. Regression Analysis
8. Sequential Patterns
9. Text Mining
Next steps1m 18s
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