Semi-Supervised and Unsupervised Machine Learning: Novel Strategies

Index

A

Augmented path

Average linkage

B

Bag-of-words

Binary independence retrieval model

Byte length normalization

C

Centroid linkage

Cluster analysis

Cluster pruning

Cluster and label

clustering

Co-training

Complete linkage

Cosine normalization

Crossover

D

Davies Bouldin index

DBSCAN

Dendogram

Dendogram inversion

Density based clustering

Divisive analysis

E

Elitism with generational replacement

Entropy

Equality subgraph

Expectation maximization

External cluster validation

F

Feasible vertex labeling

First order term co-occurrence

Flip bit mutation

G

Gap statistic

Generational replacement

Generative models

Genetic algorithms

Graph-based clustering

H

Hartigan

Hierarhical clustering

Holzinger study

Huhn Monkres theorem

Hungarian algorithm

I

Image segmentation

Information retrieval

Internal cluster evaluation

Inverse document frequency

K

k-means

Krzanowski and lai index

L

Labeled seed

Length normalization component

M

Matching

Maximum weight matching

N

Nearest neighbor

Neural gas

Normalized mutual information (NMI)

Numerical taxonomy

O

O-analysis

Okapi weighting

Optimum cluster labeling

P

Partially mapped crossover

Partitioning around medoids

Pattern

Perfect matching

Pivoted length normalization

PoBOC

Purity

R

Residual inverse document frequency

S

Scramble bit mutation

Second order term co-occurrence

Self organizing map

Self training

Semi-supervised classification

Silhouette

Simple bit mutation

Single linkage

Sliding mutation

Steady state representation

Stop word

Support vector machines ...