Course : Αλγόριθμοι ομαδοποίησης - Clustering Algorithms

Code for Sequential Clustering Algorithms

Indicative exercises for clustering

Part 1: Basic concepts in clustering

Part 10: – Graph theory-based clustering algorithms – Competitive learning clustering algorithms – Valley seeking clustering algorithms – Branch & bound clustering algorithms – Simulated annealing-based clustering algorithm

Part 11: Deterministic annealing – Genetic algorithms – Density-based clustering algorithms (DBSCAN, DENCLUE) – Spectral clustering

Part 12: Clust. algorithms for high dim. data sets – Clustering combination

Part 13: Cluster validity - Clustering tendency

Part 2: Proximity functions/measures

Part 3: Proximity measures for mixed-valued vectors – Fuzzy measures – Missing data issue – Short introduction to clustering algorithms – Sequential clustering algorithms

Part 4: Sequential Clustering algorithms (TTSAS, Maxmin) – Hard CFO clustering algorithms (k-means)

Part 5: Hard clustering: k-medoids algorithms (PAM, CLARA, CLARANS) – Probabilistic clustering (EM algorithm)

Part 6: Fuzzy CFO clustering algorithms - Basic concepts from Optimization Theory

Part 7: Possibilistic CFO clustering algorithms – Discussion on CFO clust. Algorithms – Introduction to hierarchical clustering algorithms

Part 8: Matrix-based and graph-based agglomerative hierarchical clustering algorithms

Part 9: Divisive hierarchical clustering algorithms - Hierarchical algorithms for large data sets (CURE, ROCK, Chameleon)