Join Barton Poulson for an in-depth discussion in this video Sequence mining in R, part of Data Science Foundations: Data Mining.
- [Voiceover] Now we're going to be doing an analysis…to check for changes in response methods.…We're going to use something called a hidden Markov model,…and what that does is it looks for whether people are…changing between different states or different…methods of responding.…The data set that we're going to be using is speed.csv.…It's about reactions to a judgment task…in a psychology experiment.…And the idea is that people are either responding quickly…or they're responding accurately,…that there are these two qualitatively different methods.…
And we're going to use the hidden Markov models…to see if two different states matches the data.…We're going to first load two packages:…pacman, to simply manage the packages, but also depmixS4.…That's the one that's going to allow us to do…the hidden Markov model.…And I'm going to be using a data set that's…from that package called speed,…and I'll be using that, by the way,…in my other demonstrations, but I'll be…importing it as a CSV in those ones.…So let's take a quick look at speed.…
Author
Barton Poulson
Released
9/6/2016Barton 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
Duration
Views
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Introduction
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Welcome1m 9s
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Exercise files45s
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1. Preliminaries
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Data mining prerequisites3m 43s
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Algorithm prerequisites4m 9s
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Software prerequisites5m 3s
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2. Data Reduction
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Goals of data reduction5m 6s
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Data for data reduction5m 31s
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Data reduction in R7m 5s
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Data reduction in Python3m 30s
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Data reduction in Orange4m 59s
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Data reduction in RapidMiner9m 24s
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3. Clustering
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Clustering goals3m 31s
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Clustering data6m 26s
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Clustering in R5m 26s
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Clustering in Python3m 5s
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Clustering in BigML12m 3s
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Clustering in Orange6m 52s
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4. Classification
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Classification goals4m 23s
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Classification data7m 32s
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Classification in R10m 22s
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Classification in Python3m 47s
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Classification in RapidMiner9m 13s
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Classification in KNIME7m 1s
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5. Anomaly Detection
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Anomaly detection goals5m 54s
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Anomaly detection data7m 31s
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Anomaly detection in R7m 3s
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Anomaly detection in Python4m 21s
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Anomaly detection in BigML4m 32s
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6. Association Analysis
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Association analysis goals6m 57s
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Association analysis in R7m 21s
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7. Regression Analysis
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Regression analysis goals3m 50s
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Regression analysis data4m 34s
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Regression analysis in R4m 4s
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Regression analysis in KNIME2m 36s
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8. Sequential Patterns
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Sequence mining goals2m 40s
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Sequence mining algorithms4m 44s
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Sequence mining in R5m 45s
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Sequence mining in Python4m 43s
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9. Text Mining
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Text mining goals3m 11s
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Text mining algorithms3m 21s
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Text mining in R8m 14s
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Text mining in Python8m 30s
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Text mining in RapidMiner8m 54s
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Conclusion
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Next steps1m 18s
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Video: Sequence mining in R