Preface |
Introduction to Data Mining / 1: |
Introduction / 1.1: |
Knowledge Discovery and Data Mining / 1.2: |
Data Compression / 1.3: |
Information Retrieval / 1.4: |
Text Mining / 1.5: |
Web Mining / 1.6: |
Image Mining / 1.7: |
Classification / 1.8: |
Clustering / 1.9: |
Rule Mining / 1.10: |
String Matching / 1.11: |
Bioinformatics / 1.12: |
Data Warehousing / 1.13: |
Applications and Challenges / 1.14: |
Conclusions and Discussion / 1.15: |
References |
Soft Computing / 2: |
What is Soft Computing? / 2.1: |
Relevance / 2.2.1: |
Fuzzy sets / 2.2.2: |
Neural networks / 2.2.3: |
Neuro-fuzzy computing / 2.2.4: |
Genetic algorithms / 2.2.5: |
Rough sets / 2.2.6: |
Wavelets / 2.2.7: |
Role of Fuzzy Sets in Data Mining / 2.3: |
Granular computing / 2.3.1: |
Association rules / 2.3.3: |
Functional dependencies / 2.3.4: |
Data summarization / 2.3.5: |
Image mining / 2.3.6: |
Role of Neural Networks in Data Mining / 2.4: |
Rule extraction / 2.4.1: |
Rule evaluation / 2.4.2: |
Clustering and self-organization / 2.4.3: |
Regression / 2.4.4: |
Information retrieval / 2.4.5: |
Role of Genetic Algorithms in Data Mining / 2.5: |
Role of Rough Sets in Data Mining / 2.5.1: |
Role of Wavelets in Data Mining / 2.7: |
Role of Hybridizations in Data Mining / 2.8: |
Multimedia Data Compression / 2.9: |
Information Theory Concepts / 3.1: |
Discrete memoryless model and entropy / 3.2.1: |
Noiseless Source Coding Theorem / 3.2.2: |
Classification of Compression Algorithms / 3.3: |
A Data Compression Model / 3.4: |
Measures of Compression Performance / 3.5: |
Compression ratio and bits per sample / 3.5.1: |
Quality metric / 3.5.2: |
Coding complexity / 3.5.3: |
Source Coding Algorithms / 3.6: |
Run-length coding / 3.6.1: |
Huffman coding / 3.6.2: |
Principal Component Analysis for Data Compression / 3.7: |
Principles of Still Image Compression / 3.8: |
Predictive coding / 3.8.1: |
Transform coding / 3.8.2: |
Wavelet coding / 3.8.3: |
Image Compression Standard: JPEG / 3.9: |
The JPEG Lossless Coding Algorithm / 3.10: |
Baseline JPEG Compression / 3.11: |
Color space conversion / 3.11.1: |
Source image data arrangement / 3.11.2: |
The baseline compression algorithm / 3.11.3: |
Decompression process in baseline JPEG / 3.11.4: |
JPEG2000: Next generation still picture coding standard / 3.11.5: |
Text Compression / 3.12: |
The LZ77 algorithm / 3.12.1: |
The LZ78 algorithm / 3.12.2: |
The LZW algorithm / 3.12.3: |
Other applications of Lempel-Ziv coding / 3.12.4: |
Some definitions and preliminaries / 3.13: |
String matching problem / 4.1.2: |
Brute force string matching / 4.1.3: |
Linear-Order String Matching Algorithms / 4.2: |
String matching with finite automata / 4.2.1: |
Knuth-Morris-Pratt algorithm / 4.2.2: |
Boyer-Moore algorithm / 4.2.3: |
Boyer-Moore-Horspool algorithm / 4.2.4: |
Karp-Rabin algorithm / 4.2.5: |
String Matching in Bioinformatics / 4.3: |
Approximate String Matching / 4.4: |
Basic definitions / 4.4.1: |
Wagner-Fischer algorithm for computation of string distance / 4.4.2: |
Text search with k-differences / 4.4.3: |
Compressed Pattern Matching / 4.5: |
Classification in Data Mining / 4.6: |
Decision Tree Classifiers / 5.1: |
ID3 / 5.2.1: |
IBM IntelligentMiner / 5.2.2: |
Serial PaRallelizable INduction of decision Trees (SPRINT) / 5.2.3: |
RainForest / 5.2.4: |
Overfitting / 5.2.5: |
PrUning and BuiLding Integrated in Classification (PUBLIC) / 5.2.6: |
Extracting classification rules from trees / 5.2.7: |
Fusion with neural networks / 5.2.8: |
Bayesian Classifiers / 5.3: |
Bayesian rule for minimum risk / 5.3.1: |
Naive Bayesian classifier / 5.3.2: |
Bayesian belief network / 5.3.3: |
Instance-Based Learners / 5.4: |
Minimum distance classifiers / 5.4.1: |
k-nearest neighbor (k-NN) classifier / 5.4.2: |
Locally weighted regression / 5.4.3: |
Radial basis functions (RBFs) / 5.4.4: |
Case-based reasoning (CBR) / 5.4.5: |
Granular computing and CBR / 5.4.6: |
Support Vector Machines / 5.5: |
Fuzzy Decision Trees / 5.6: |
Rule generation and evaluation / 5.6.1: |
Mapping of rules to fuzzy neural network / 5.6.3: |
Results / 5.6.4: |
Clustering in Data Mining / 5.7: |
Distance Measures and Symbolic Objects / 6.1: |
Numeric objects / 6.2.1: |
Binary objects / 6.2.2: |
Categorical objects / 6.2.3: |
Symbolic objects / 6.2.4: |
Clustering Categories / 6.3: |
Partitional clustering / 6.3.1: |
Hierarchical clustering / 6.3.2: |
Leader clustering / 6.3.3: |
Scalable Clustering Algorithms / 6.4: |
Clustering large applications / 6.4.1: |
Density-based clustering / 6.4.2: |
Grid-based methods / 6.4.3: |
Other variants / 6.4.5: |
Soft Computing-Based Approaches / 6.5: |
Evolutionary algorithms / 6.5.1: |
Clustering with Categorical Attributes / 6.6: |
Sieving Through Iterated Relational Reinforcements (STIRR) / 6.6.1: |
Robust Hierarchical Clustering with Links (ROCK) / 6.6.2: |
c-modes algorithm / 6.6.3: |
Hierarchical Symbolic Clustering / 6.7: |
Conceptual clustering / 6.7.1: |
Agglomerative symbolic clustering / 6.7.2: |
Cluster validity indices / 6.7.3: |
Association Rules / 6.7.4: |
Candidate Generation and Test Methods / 7.1: |
A priori algorithm / 7.2.1: |
Partition algorithm / 7.2.2: |
Some extensions / 7.2.3: |
Depth-First Search Methods / 7.3: |
Interesting Rules / 7.4: |
Multilevel Rules / 7.5: |
Online Generation of Rules / 7.6: |
Generalized Rules / 7.7: |
Scalable Mining of Rules / 7.8: |
Other Variants / 7.9: |
Quantitative association rules / 7.9.1: |
Temporal association rules / 7.9.2: |
Correlation rules / 7.9.3: |
Localized associations / 7.9.4: |
Optimized association rules / 7.9.5: |
Fuzzy Association Rules / 7.10: |
Rule Mining with Soft Computing / 7.11: |
Connectionist Rule Generation / 8.1: |
Neural models / 8.2.1: |
Neuro-fuzzy models / 8.2.2: |
Using knowledge-based networks / 8.2.3: |
Modular Hybridization / 8.3: |
Rough fuzzy MLP / 8.3.1: |
Modular knowledge-based network / 8.3.2: |
Evolutionary design / 8.3.3: |
Multimedia Data Mining / 8.3.4: |
Keyword-based search and mining / 9.1: |
Text analysis and retrieval / 9.2.2: |
Mathematical modeling of documents / 9.2.3: |
Similarity-based matching for documents and queries / 9.2.4: |
Latent semantic analysis / 9.2.5: |
Soft computing approaches / 9.2.6: |
Content-Based Image Retrieval / 9.3: |
Color features / 9.3.2: |
Texture features / 9.3.3: |
Shape features / 9.3.4: |
Topology / 9.3.5: |
Multidimensional indexing / 9.3.6: |
Results of a simple CBIR system / 9.3.7: |
Video Mining / 9.4: |
MPEG-7: Multimedia content description interface / 9.4.1: |
Content-based video retrieval system / 9.4.2: |
Search engines / 9.5: |
Bioinformatics: An Application / 9.5.2: |
Preliminaries from Biology / 10.1: |
Deoxyribonucleic acid / 10.2.1: |
Amino acids / 10.2.2: |
Proteins / 10.2.3: |
Microarray and gene expression / 10.2.4: |
Information Science Aspects / 10.3: |
Protein folding / 10.3.1: |
Protein structure modeling / 10.3.2: |
Genomic sequence analysis / 10.3.3: |
Homology search / 10.3.4: |
Clustering of Microarray Data / 10.4: |
First-generation algorithms / 10.4.1: |
Second-generation algorithms / 10.4.2: |
Role of Soft Computing / 10.5: |
Predicting protein secondary structure / 10.6.1: |
Predicting protein tertiary structure / 10.6.2: |
Determining binding sites / 10.6.3: |
Classifying gene expression data / 10.6.4: |
Index / 10.7: |
About the Authors |
Preface |
Introduction to Data Mining / 1: |
Introduction / 1.1: |