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• Classic-Fiction

Thomsen, E., OLAP Solution: Building Multidimensional Information System, John Wiley, New York, 1997.

Tufte, E. R., Beautiful Evidence, 2nd edition, Graphic Press, LLC, CT, 2007.

Two Crows Corp., Introduction to Data Mining and Knowledge Discovery, Two Crows Corporation, Maryland, 2005.

Wong, P. C., Visual Data Mining, IEEE Computer Graphics and Applications, Vol. 14, 1999, pp. 20–21.

INDEX

A posterior distribution

A priori algorithm

Partition-based

Sampling-based

Incremental updating

Concept hierarchy

A prior distribution

A priori knowledge

Approximating functions

Activation function

Agglomerative clustering algorithms

Aggregation

Allela

Alpha cut

Alternation

Analysis of variance (ANOVA)

Anchored visualization

Andrews’s curve

Approximate reasoning

Approximation by rounding

Artificial neural network (ANN)

Artificial neural network, architecture

feedforward

recurrent

Competitive

Self-organizing map (SOM)

Artificial neuron

Association rules

Apriori

FPgrowth

Classification based on multiple association rules (CMAR)

Asymptotic consistency

Autoassociation

Authorities

Bar chart

Bayesian inference

Bayesian networks

Bayes theorem

Binary features

Bins

Bins cutoff

Bootstrap method

Boxplot

Building blocks

Candidate counting

Candidate generation

Cardinality

Cases reduction

Causality

Censoring

Centroid

Chameleon

Change detection

Chernoff’s faces

ChiMerge technique

Chi-squared test

Chromozome

Circular coordinates

City block distance

Classification

CART

C4.5

ID3

k-NN

SVM

Classifier

CLS

Cluster analysis

Cluster feature vector (CF)

Clustering

BIRCH

DBSCAN

Validation

k-means

k-medoids

Incremental

Using genetic algorithms

Clustering tree

Competitive learning rule

Complete-link method

Confidence

Confirmatory visualization

Confusion matrix

Contingency table

Control theory

Core

Correlation coefficient

Correspondence analysis

Cosine correlation

Covariance matrix

Crisp approximation

Crossover

Curse of dimensionality

Data cleansing

Data scrubbing

Data collection

Data constellations

Data cube

Data discovery

Data integration

Data mart

Data mining

Privacy

Security

Regal aspects

Data mining process

Data mining roots

Data mining tasks

Data preprocessing

Data quality

Data set

Iris

messy

preparation

quality

raw

semistructured

structured

temporal

time-dependent

transformation

unstructured

Data set dimensions

cases

columns

feature values

Data sheet

Data smoothing

Data types,

alphanumeric

categorical

dynamic

numeric

symbolic

Data warehouse

Data representation

Decimal scaling

Decision node

Decision rules

Decision tree

Deduction

Default class

Defuzzification

Delta rule

Dendogram

Dependency modeling

Descriptive accuracy

Descriptive data mining

Designed experiment

Deviation detection

Differences

Dimensional stacking

Directed acyclic graph (DAG)

Discrete optimization

Discrete Fourier Transform

Discrete Wavelet Transform

Discriminant function

Distance error

Distance measure

Distributed data mining

Distributed DBSCAN

Divisible clustering algorithms

Document visualization

Domain-specific knowledge

Don’t care symbol

Eigenvalue

Eigenvector

Empirical risk

Empirical risk minimization (ERM)

Encoding

Encoding scheme

Ensemble learning

Bagging

Boosting

AdaBoost

Entropy

Error back-propagation algorithm

Error energy

Error-correction learning

Error rate

Euclidean distance

Exponential moving average

Exploratory analysis

Exploratory visualizations

Extension principle

False acceptance rate (FAR)

False reject rate (FRT)

Fault tolerance

Feature discretization

Features composition

Features ranking

Features reduction

Features selection

Relief

Filtering data

First-principle models

Fitness evaluation

Free parameters

F-list

FP-tree

Function approximation

Fuzzy inference systems

Fuzzy logic

Fuzzy number

Fuzzy relation

containment

equality

Fuzzy rules

Fuzzy set

Fuzzy set operation

complement