Join Barton Poulson for an in-depth discussion in this video Classification in KNIME, part of Data Science Foundations: Data Mining.
- [Narrator] I'm here in KNIME,…and I have my entire workspace set out already,…but let me show you how you can get…these various bits and pieces.…What I have in here are six different nodes,…and you can click on a node,…and you get information off to the right…under Node Description.…Now, if you want to add a node,…what you need to do is come down here to the node repository…and you can either navigate to the one you want,…so for instance, the first one I need is CSV Reader.…That's going to be input ouput.…It's going to be under Read,…and there it is right there, and you can drag that in,…or another way to do it is to simply search for it.…
I can type in, "csv" right here, and there it is.…I can drag that in too.…Now, I bring the nodes in, and I simply drag to connect…from one node to the next.…That's pretty easy, and you can rearrange however you want.…If you have a lot of nodes, then this outline view here…at the bottom, can be really helpful,…because it shows you the visible part,…and then you can drive that around to see the rest of it.…
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
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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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