B – Bayes belief networks
C – Characterization
D – Deep learning
E – Ensemble learning
F – Forests (i.e., random forests)
G – Gaussian mixture models
H – Hadoop
I – Informatics
JJ – JSON and JAQL
K – K-anything in data mining
L – Local linear embedding (LLE)
M – Multiple weak classifiers
N – Novelty detection
O – One-class classifier
P – Profiling (data profiling)
Q – Quantified and tracked
R – Recommender engines
S – Support Vector Machines (SVM)
T – Tree indexing schemes
U – Unsupervised exploratory analysis
V – Visual analytics
W – WEKA (Waikato Environment for Knowledge Analysis)
X – XML (specifically Predictive Modeling Markup Language )
Y – YarcData
ZZ – Zero bias, Zero variance
View detailed explanation
Full list of Big Data entries in TodoBI
Our Big Data Aproach architecture for analytics
See in Data Science Central
