Join Barton Poulson for an in-depth discussion in this video Sequence mining goals, part of Data Science Foundations: Data Mining.
- [Instructor] Sequence mining may not be…a familiar procedure to a lot of people,…and so it helps to think about what some of the goals…of this particular approach are.…Specifically, what you're doing is you're looking for chains…in the data, chains that repeat.…And you can think of it this way,…here we've got a whole lot of letters.…It's just a random blob of code.…And we wanna see if there are any regularities in that…where we can say, well, if this and this happen,…then this and this will probably happen next.…As it turns out, there are some regularities in this data.…You may not be able to see them well,…but we can highlight them.…
b, g, b, p, i, x.…And there it is at the top.…And you know what, there it is again,…split over the second and third lines.…And there it is in the fourth line.…And there it is in the sixth line.…And so it's in there, but it's really hard…to see on your own,…and obviously this is just a small number.…If you had say, for instance, 10,000 words…it would be a lot harder to do by simply looking.…
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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