Join Barton Poulson for an in-depth discussion in this video Association analysis in R, part of Data Science Foundations: Data Mining.
When you open up in Rstudio, the first thing…we're going to need to do is install…a couple of packages that we use for…association rules learning, or association analysis.…The first one is called arules…for association rules learning,…that's the one that does the actual calculations,…and there's another one, arulesViz,…for doing a whole series of visualizations,…which actually are one of the great strengths…of this approach.…So if you have Pacman installed already,…then you can do this,…and what this will do is it will download those packages…if you don't have them, and either way,…once it's downloaded them, or if you have them already,…it will load them up, which it's done for me.…
Now, we're going to use a data set called Groceries…that's in this packet.…We have a similar data set that we'll be using…in some other demonstrations as well.…We're going to read data ("Groceries"),…which is from the arules package,…and you can see that it's showing up there on the right.…I'm going to show you something that's a little unusual…
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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