Data Mining Mehmed Kantardzic (good english books to read .txt) 📖
- Author: Mehmed Kantardzic
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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.
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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
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