Understanding Lecture 20 Cs 432 Data Mining
Let's dive into the details surrounding Lecture 20 Cs 432 Data Mining. clustering validation.
Key Takeaways about Lecture 20 Cs 432 Data Mining
- K-medoids, k-medians, k-modes, hierarchical algorithms, merge criteria. single link, complete link, Ward's method.
- BIRCH, introduction to density based clustering.
- k-means clustering alogrithm, LUMS.
- DBSCAN, case study.
- reconstruction based outlier detection, auto-encoder for outlier detection.
Detailed Analysis of Lecture 20 Cs 432 Data Mining
Clustering validation. Outlier detection, distance based outliers, LOF. Chinese Whispers clustering algorithm.
Agglomerative clustering, BIRCH.
That wraps up our extensive overview of Lecture 20 Cs 432 Data Mining.
