CS代考程序代写 algorithm LECTURE 5 TERM 2:
LECTURE 5 TERM 2:
MSIN0097
Predictive Analytics
A P MOORE
A – B – C- D ALGORITHMIC APPROACHES
A. ClAssification
B. Regression
Super vised
C. Clustering
D. Decomposition
Unsuper vised
A – B – C- D ALGORITHMIC APPROACHES
A. ClAssification
C. Clustering
Hidden variables
Density estimation Manifolds
B. Regression
Super vised
D. Decomposition
Subspaces
Unsuper vised
CL ASSIFIC ATION CATEGORICAL VARIABLE
CLASSIFICATION VS CLUSTERING CATEGORICAL VARIABLE
CLASSIFICATION VS CLUSTERING
MSIN0097
Types of clustering
CLUSTERING TAXONOMY
AGGLOMERATIVE
AGGLOMERATIVE
AGGLOMERATIVE
AGGLOMERATIVE
AGGLOMERATIVE DENDROGRAM
Dendrogram
DIVISIVE
DIVISIVE
DIVISIVE
DIVISIVE
PARTITIONAL
PARTITIONAL
MSIN0097
A simple algorithm
K-MEANS LLOYD–FORGY ALGORITHM
K-MEANS LLOYD–FORGY ALGORITHM
DECISION BOUNDARIES VORONOI TESSELLATION
Vector quantization
K-MEANS ALGORITHM
INITIALIZATION
223.3
INITIALIZATION
223. 3 237. 5
ACCELERATING K-MEANS
BAD CLUSTERS
SELECTING K – INERTIA
SELECTING K – INERTIA
SILHOUETTE SCORE
SELECTING K – SILHOUETTE SCORE
SILHOUETTE DIAGRAMS
ELLIPSOIDAL DISTRIBUTED DATA
ELLIPSOIDAL DISTRIBUTED DATA
CLUSTERING FOR SEGMENTATION
CLUSTERING FOR SEGMENTATION
CLUSTERING METHODS
Sourec: https://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html
CLUSTERING TAXONOMY
Agglomerative
— BIRCH
— Mean-shift
— Affinity propagation
Divisive
— Spectral clustering — Graph-cuts
Partitional
— k-means
— Mixture models
— Gaussian mixture models (GMMs)
DBSC AN
DBSC AN
DECISION BOUNDARY
K-MEANS?
MEAN SHIFT
GRAPH PARTITIONING
LECTURE 5 TERM 2:
MSIN0097
Predictive Analytics
A P MOORE
