- [Instructor] Now imagine a situation, very reduce here,…where it's possible to purchase five different items,…we'll just call them A through E,…and we'll have a few different shoppers here…with their market baskets.…First one has A and B.…Second one has A and C.…A, D, and E, B, C, and D,…and basket number five has A, B, D, and E.…And so, what we're looking at is can we use…any one of the items in these baskets to accurately predict…whether a person will get some other item?…Well, there are two general steps to this process…in an association analysis.…
Number one is you want to construct what are called…frequent itemsets, and what that is…is a combination of items that appear together,…and you calculate a index number called "support"…that indicates how often these things appear together.…I'll show you how that works in a moment.…The next step to this is called rule generation,…and what this is is a collection of if/then statements,…where you calculate something called "confidence".…That's a metric that's similar to conditional probability.…
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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