Data Mining (148 page)

Lin, T. Y., N. Cerone,
Rough Sets and Data Mining
, Kluwer Academic Publishers, Inc., Boston, 1997.

Maimon, O., M. Last,
Knowledge Discovery and Data Mining: The Info-Fuzzy Network (IFN) Methodology
, Kluwer Academic Publishers, Boston, MA, 2001.

Mendel, J., Fuzzy Logic Systems for Engineering: A Tutorial,
Proceedings of the IEEE
, Vol. 83, No. 3, 1995, pp. 345–377.

Miyamoto, S.,
Fuzzy Sets in Information Retrieval and Cluster Analysis
, Kluwer Academic Publishers, Dordrecht, 1990.

Munakata, T.,
Fundamentals of the New Artificial Intelligence: Beyond Traditional Paradigm
, Springer, New York, 1998.

Özyer, T., R. Alhajj, K. Barker, Intrusion Detection by Integrating Boosting Genetic Fuzzy Classifier and Data Mining Criteria for Rule Pre-Screening,
Journal of Network and Computer Applications
, Vol. 30, No. 1, 2007, pp. 99–113.

Pal, S. K., S. Mitra,
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
, John Wiley & Sons, Inc., New York, 1999.

Pedrycz, W., F. Gomide,
An Introduction to Fuzzy Sets: Analysis and Design
, The MIT Press, Cambridge, 1998.

Pedrycz, W., J. Waletzky, Fuzzy Clustering with Partial Supervision,
IEEE Transactions on System, Man, and Cybernetics
, Vol. 27, No. 5, 1997, pp. 787–795.

Yager, R. R., Targeted E-Commerce Marketing Using Fuzzy Intelligent Agents,
IEEE Intelligent Systems
, November/December 2000, pp. 42–45.

Yeung, D. S., E. C. C. Tsang, A Comparative Study on Similarity-Based Fuzzy Reasoning Methods,
IEEE Transactions on System, Man, and Cybernetics
, Vol. 27, No. 2, 1997, pp. 216–227.

Zadeh, L. A., Knowledge Representation in Fuzzy Logic,
IEEE Transactions on Knowledge and Data Engineering
, Vol. 1, No. 1, 1989, pp. 89–99.

Zadeh, L. A., Fuzzy Logic = Computing with Words,
IEEE Transactions on Fuzzy Systems
, Vol. 4, No. 2, 1996, pp. 103–111.

CHAPTER 15

Barry, A. M. S.,
Visual Intelligence
, State University of New York Press, New York, 1997.

Bohlen, M., 3D Visual Data Mining—Goals and Experiences,
Computational Statistics & Data Analysis
, Vol. 43, No. 4, 2003, pp. 445–469.

Buja, A., D. Cook, D. F. Swayne, Interactive High-Dimensional Data Visualization, 1996,
http://www.research.att.com/andreas/xgobi/heidel
.

Chen, C., R. J. Paul, Visualizing a Knowledge Domain’s Intellectual Structure,
Computer
, Vol. 36, No. 3, 2001, pp. 65–72.

Draper, G. M., L. Y. Livnat, R. F. Riesenfeld, A Survey of Radial Methods for Information Visualization,
IEEE Transactions on Visualization and Computer Graphics
, Vol. 15, No. 5, 2009, pp. 759–776.

Eick, S. G., Visual Discovery and Analysis,
IEEE Transactions on Visualization and Computer Graphics
, Vol. 6, No. 1, 2000a, pp. 44–57.

Eick, S. G., Visualizing Multi-Dimensional Data,
Computer Graphics
, Vol. 34, 2000b, pp. 61–67.

Elmqvist, N., J. Fekete, Hierarchical Aggregation for Information Visualization: Overview, Techniques and Design Guidelines,
IEEE Transactions on Visualization and Computer Graphics
, Vol. 16, No. 3, 2010, pp. 439–454.

Estrin, D., et al., Network Visualization with Nam, the VINT Network Animator,
Computer
, Vol. 33, No. 11, 2000, pp. 63–68.

Faloutsos, C., K. Lin, FastMap: A Fast Algorithm for Indexing, Data-Mining and Visualization of Traditional and Multimedia Datasets, Proceedings of SIGMOD’95 Conference, San Jose, 1995, pp. 163–174.

Fayyad, U., G. Georges Grinstein, A. Wierse,
Information Visualization in Data Mining and Knowledge Discovery
, 1st edition, Morgan Kaufmann, San Francisco, CA, 2001.

Fayyad, U. M., G. G. Grinstein, A. Wierse,
Information Visualization in Data Mining and Knowledge Discovery
, Academic Press, San Diego, 2002a.

Fayyad, U., G. G. Grinstein, A. Wierse, eds.,
Information Visualization in Data Mining and Knowledge Discovery
, Morgan Kaufmann Publishers, San Francisco, CA, 2002b.

Ferreira de Oliveira, M. C., H. Levkowitz, From Visual Data Exploration to Visual Data Mining: A Survey,
IEEE Transactions on Visualization and Computer Graphics
, Vol. 9, No. 3, 2003, pp. 378–394.

Gallaghar, R. S.,
Computer Visualization: Graphics Techniques for Scientific and Engineering Analysis
, CRC Press, Inc., Boca Raton, FL, 1995.

Hinneburg, A., D. A. Keim, M. Wawryniuk, HD-Eye: Visual Mining of High-Dimensional Data,
IEEE Computer Graphics and Applications
, Vol. 19, 1999, pp. 22–31.

Hofman, P., Radviz, 1997,
http://www.cs.uml.edu/phoffman/viz
.

IBM, Parallel Visual Explorer at Work in the Money Market, 1997,
http://www.ibm.com/news/950203/pve-03html
.

Inselberg, A., B. Dimsdale, Visualizing Multi-Variate Relations with Parallel Coordinates, Proceedings of the Third International Conference on Human-Computer Interaction, New York, 1989, pp. 460–467.

Mackinlay, J. D., Opportunities for Information Visualization,
IEEE Computer Graphics and Applications
, Vol. 20, 2000, pp. 22–23.

Masseglia, F., P. Poncelet, T. Teisseire,
Successes and New Directions in Data Mining
, Idea Group Inc., Hershey, PA, 2007.

Plaisant, C., The Challenge of Information Visualization Evaluation, IEEE Proc. of Advanced Visual Interfaces, Gallipoli, Italy, 2004, pp. 109–116.

Pu, P., G. Melissargos, Visualizing Resource Allocation Tasks,
IEEE Computer Graphics and Applications
, Vol. 4, 1997, pp. 6–9.

Roth, S. F., M. C. Chuah, S. Kerpedjiev, J. A. Kolojejchick, P. Lukas, Towards an Information Visualization Workspace: Combining Multiple Means of Expressions,
Human-Computer Interaction Journal
, Vol. 12, 1997, pp. 61–70.

Spence, R.,
Information Visualization
, Addison Wesley, Harlow, UK, 2001.

Tergan, S., T. Keller,
Knowledge and Information Visualization: Searching for Synergies
, Springer, Secaucus, NJ, 2005.

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

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 Bayesian classifier

N-dimensional data

N-dimensional space

N-dimensional visualization

N-fold cross-validation

Necessity measure

Negative border

Neighbor number (NN)

Neuro-Fuzzy system

Nominal scale

Normalization

NP hard problem

Null hypothesis

Objective function

Observational approach

OLAP (Online analytical processing)

Optimization

Ordinal scale

Outlier analysis

Outlier detection

Outlier detection, distance based

Overfitting (overtraining)

PageRank algorithm

Parabox

Parallel coordinates

Parameter identification

Partially matched crossover (PMC)

Partitional clustering

Pattern

Pattern association

Pattern recognition

Pearson correlation coefficient

Perception

Perceptron

Pie chart

Piecewise aggregate approximation (PAA)

Pixel-oriented visualization

Population

Possibility measure

Postpruning

Prediction

Predictive accuracy

Predictive data mining

Predictive regression

Prepruning

Principal Component Analysis (PCA)

Principal components

Projected database

Pruning decision tree

Radial visualization (Radviz)

Random variable

Rao’s coefficient

Ratio scale

Ratios

Receiver operating characteristic (ROC)

Receiver operating characteristic (ROC) curve

Regression

Logistic

Linear

Nonlinear

Multiple

Regression equation

Resampling methods

Resubstitution method

Return on investment (ROI) chart

Risk functional

Rotation method

RuleExchange

RuleGeneralization

RuleSpecialization

RuleSplit

Sample

Sampling

average

incremental

inverse

random

stratified

systematic

Saturating linear function

Scaling

Scatter plot

Schemata

fitness

length

order

Scientific visualization

Scrubbing

Sensitivity

Sequence

Sequence mining

Sequential pattern

Similarity measure

Simple matching coefficient (SMC)

Single-link method

Smoothing data

Spatial data mining

Autoregressive model

Spatial outlier

Specificity

Split-info function

SQL (Structured query language)

SSE (Sum of squares of the errors)

Standard deviation

Star display

Statistics

Statistical dependency

Statistical inference

Statistical learning theory (SLT)

Statistical methods

Statistical testing

Stochastic approximation

Stopping rules

Strong rules

Structure identification

Structural risk minimization (SRM)

Summarization

Supervised learning

Support

Survey plot

Survival data

Synapse

System identification

Tchebyshev distance

Temporal data Mining

Sequences

Time series

Test of hypothesis

Testing sample

Text analysis

Text database

Text mining

Text-refining

Time lag (time window)

Time series, multivariate

Time series, univariate

Training sample

Transduction

Traveling salesman problem (TSP)

Trial and error

True risk functional

Ubiquitous data mining

Underfitting

Unobserved inputs

Unsupervised learning

Value reduction

Variables

continuous

discrete

categorical

dependent

independent

nominal

numeric

ordinal

periodic

unobserved

Variance

Variogram cloud technique

Vapnik-Chervonenkis (VC) theory

Vapnik-Chervonenkis (VC) dimension

Visual clustering

Visual data mining

Visualization

Visualization tool

Voronoi diagram

Web mining

content

HITS(Hyperlink-Induced Topic Search) algorithm

LOGSOM algorithm

path-traversal patterns

structure

usage

Web page content

Web page design

Web page quality

Web site design

Web site structure

Widrow-Hoff rule

Winner-take-all rule

XOR problem