Join Barton Poulson for an in-depth discussion in this video Association analysis data, part of Data Science Foundations: Data Mining.
- [Voiceover] When you're conducting an…association analysis, you have a choice of a…pretty broad range of algorithms that you can use.…And the purpose of all these algorithms is to help you…find itemsets, things that appear frequently in baskets,…and to generate the rules that can be used to predict…the things that go in those baskets based on other things.…Now, of the algorithms that are available,…probably the most common is Apriori.…Another very common one is Eclat…which stands for equivalence class transformations.…
There's also FP-growth.…That stands for frequent pattern growth.…There's RElim, for recursive elimination.…SaM, for split and merge.…And JIM, for Jaccard Itemset Mining.…Some of these only find itemsets,…they don't do the rule generation step,…and so, you would have to use another one for that, but…I'm going to show you ones that do it all.…Probably the most common one is Apriori.…What Apriori does is it calculates support for…single-item itemsets.…
So, for one thing at a time, it gets the support,…
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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