Data Mining by Mehmed Kantardzic (good book recommendations TXT) 📕

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CHAPTER 15

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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

cartesian product

concentration

dilation

intersection

normalization

union

Fuzzification

Gain function

Gain-ratio function

Gaussian membership function

Gene

Generalization

Generalized Apriori

Generalized modus ponens

Genetic algorithm

Genetic operators

crossover

mutation

selection

Geometric projection visualization

GINI index

Glyphs

Gradviz

Graph mining

Centrality

Closeness

Betweenness

Graph compression

Graph clustering

Gray coding

Greedy optimization

Grid-based rule

Growth function

Hamming distance

Hamming networks

Hard limit function

Heteroassociation

Hidden node

Hierarchical clustering

Hierarchical visualization techniques

Histogram

Holdout method

Hubs

Hyperbolic tangent sigmoid

Hypertext

Icon-based visualization

Induction

Inductive-learning methods

Inductive machine learning

Inductive principle

Info function

Information visualization

Information retrieval (IR)

Initial population

Interesting association rules

Internet searching

Interval scale

Inverse document frequency

Itemset

Jaccard coefficient

Kernel function

Knowledge distillation

Large data set

Large itemset

Large reference sequence

Lateral inhibition

Latent semantic analysis (LSA)

Learning machine

Learning method

Learning process

Learning tasks

Learning theory

Learning rate

Learning system

Learning with teacher

Learning without teacher

Leave-one-out method

Lift chart

Line chart

Linear discriminant analysis (LDA)

Linguistic variable

Local gradient

Locus

Logical classification models

Log-linear models

Log-sigmoid function

Longest common sequence (LCS)

Loss function

Machine learning

Mamdani model

Manipulative visualization

Multivariate analysis of variance (MANOVA)

Market basket analysis

Markov Model (MM)

Hidden Markov Model (HMM)

Max-min composition

MD-pattern

Mean

Median

Membership function

Metric distance measure

Minkowski metric

Min-max normalization

Misclassification

Missing data

Mode

Model

estimation

selection

validation

verification

Momentum constant

Moving average

Multidimensional association rules

Multifactorial evaluation

Multilayer perceptron

Multiple discriminant analysis

Multiple regression

Multiscape

Mutual neighbor distance (MND)

Naïve