Join Barton Poulson for an in-depth discussion in this video Clustering goals, part of Data Science Foundations: Data Mining.
- [Narrator] Clustering or Cluster Analysis is one…of the foundational and most important tasks…that you can do in Data Mining.…The general idea is to take an entire collection…of cases or observations and put them together…so like goes with like.…Now the important thing to remember is…these are not natural groupings,…rather these are Groups of Convenience.…They're not the platonic real universals…that cut nature on its joints.…They're groups of cases that are similar to each other,…but with a pragmatic goal.…
You group the cases to accomplish a specific purpose.…And the real merit of the grouping…is how well does it serve that purpose.…So you can think of this as a version of Functionalism.…And the question is, which cases can be treated…in the same way depending on a specific purpose?…Now clustering serves really important purposes…in a lot of different fields.…To give you a few examples, in Marketing…the idea here is that you make offers…to people who are in similar categories.…
You give them the same kind of ads.…
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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