# 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

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