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1.

図書

図書
Alex A. Freitas
出版情報: Berlin : Springer, c2002  xiv, 264 p. ; 24 cm
シリーズ名: Natural computing series
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2.

図書

図書
Chengqi Zhang, Shichao Zhang
出版情報: Berlin : Springer, c2002  xii, 238 p. ; 24 cm
シリーズ名: Lecture notes in computer science ; 2307 . Lecture notes in artificial intelligence
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目次情報: 続きを見る
Introduction / 1:
What Is Data Mining? / 1.1:
Why Do We Need Data Mining? / 1.2:
Knowledge Discovery in Databases (KDD) / 1.3:
Processing Steps of KDD / 1.3.1:
Feature Selection / 1.3.2:
Applications of Knowledge Discovery in Databases / 1.3.3:
Data Mining Task / 1.4:
Data Mining Techniques / 1.5:
Clustering / 1.5.1:
Classification / 1.5.2:
Conceptual Clustering and Classification / 1.5.3:
Dependency Modeling / 1.5.4:
Summarization / 1.5.5:
Regression / 1.5.6:
Case-Based Learning / 1.5.7:
Mining Time-Series Data / 1.5.8:
Data Mining and Marketing / 1.6:
Solving Real-World Problems by Data Mining / 1.7:
Summary / 1.8:
Trends of Data Mining / 1.8.1:
Outline / 1.8.2:
Association Rule / 2:
Basic Concepts / 2.1:
Measurement of Association Rules / 2.2:
Support-Confidence Framework / 2.2.1:
Three Established Measurements / 2.2.2:
Searching Frequent Itemsets / 2.3:
The Apriori Algorithm / 2.3.1:
Identifying Itemsets of Interest / 2.3.2:
Research into Mining Association Rules / 2.4:
Chi-squared Test Method / 2.4.1:
The FP-tree Based Model / 2.4.2:
OPUS Based Algorithm / 2.4.3:
Negative Association Rule / 2.5:
Focusing on Itemsets of Interest / 3.1:
Effectiveness of Focusing on Infrequent Itemsets of Interest / 3.3:
Itemsets of Interest / 3.4:
Positive Itemsets of Interest / 3.4.1:
Negative Itemsets of Interest / 3.4.2:
Searching Interesting Itemsets / 3.5:
Procedure / 3.5.1:
An Example / 3.5.2:
A Twice-Pruning Approach / 3.5.3:
Negative Association Rules of Interest / 3.6:
Measurement / 3.6.1:
Examples / 3.6.2:
Algorithms Design / 3.7:
Identifying Reliable Exceptions / 3.8:
Confidence Based Interestingness / 3.8.1:
Support Based Interestingness / 3.8.2:
Searching Reliable Exceptions / 3.8.3:
Comparisons / 3.9:
Comparison with Support-Confidence Framework / 3.9.1:
Comparison with Interest Models / 3.9.2:
Comparison with Exception Mining Model / 3.9.3:
Comparison with Strong Negative Association Model / 3.9.4:
Causality in Databases / 3.10:
Basic Definitions / 4.1:
Data Partitioning / 4.3:
Partitioning Domains of Attributes / 4.3.1:
Quantitative Items / 4.3.2:
Decomposition and Composition of Quantitative Items / 4.3.3:
Item Variables / 4.3.4:
Decomposition and Composition for Item Variables / 4.3.5:
Procedure of Partitioning / 4.3.6:
Dependency among Variables / 4.4:
Conditional Probabilities / 4.4.1:
Causal Rules of Interest / 4.4.2:
Algorithm Design / 4.4.3:
Causality in Probabilistic Databases / 4.5:
Problem Statement / 4.5.1:
Required Concepts / 4.5.2:
Preprocess of Data / 4.5.3:
Probabilistic Dependency / 4.5.4:
Improvements / 4.5.5:
Causal Rule Analysis / 4.6:
Related Concepts / 5.1:
Optimizing Causal Rules / 5.3:
Unnecessary Information / 5.3.1:
Merging Unnecessary Information / 5.3.2:
Merging Items with Identical Properties / 5.3.3:
Polynomial Function for Causality / 5.4:
Causal Relationship / 5.4.1:
Binary Linear Causality / 5.4.2:
N-ary Linear Propagating Model / 5.4.3:
Functions for General Causality / 5.4.4:
Approximating Causality by Fitting / 5.6:
Preprocessing of Data / 5.6.1:
Constructing the Polynomial Function / 5.6.2:
Association Rules in Very Large Databases / 5.6.3:
Instance Selection / 6.1:
Evaluating the Size of Instance Sets / 6.2.1:
Generating Instance Set / 6.2.2:
Estimation of Association Rules / 6.3:
Identifying Approximate Frequent Itemsets / 6.3.1:
Measuring Association Rules of Interest / 6.3.2:
Algorithm Designing / 6.3.3:
Searching True Association Rules Based on Approximations / 6.4:
Incremental Mining / 6.5:
Promising Itemsets / 6.5.1:
Searching Procedure / 6.5.2:
Competitive Set Method / 6.5.3:
Assigning Weights / 6.5.4:
Algorithm of Incremental Mining / 6.5.5:
Improvement of Incremental Mining / 6.6:
Conditions of Termination / 6.6.1:
Anytime Search Algorithm / 6.6.2:
Association Rules in Small Databases / 6.7:
Problems Faced by Utilizing External Data / 7.1:
Our Approach / 7.2.2:
External Data Collecting / 7.3:
Available Tools / 7.3.1:
Indexing by a Conditional Associated Semantic / 7.3.2:
Procedures for Similarity / 7.3.3:
A Data Preprocessing Framework / 7.4:
Pre-analysis: Selecting Relevant and Uncontradictable Collected Data-Sources / 7.4.1:
Post-analysis: Summarizing Historical Data / 7.4.2:
Synthesizing Selected Rules / 7.4.3:
Refining Rules Mined in Small Databases / 7.5.1:
Conclusion and Future Work / 7.7:
Conclusion / 8.1:
Future Work / 8.2:
References
Subject Index
Introduction / 1:
What Is Data Mining? / 1.1:
Why Do We Need Data Mining? / 1.2:
3.

図書

図書
Wojciech Szpankowski
出版情報: New York : J. Wiley, c2001  xxii, 551 p. ; 25 cm
シリーズ名: Wiley-Interscience series in discrete mathematics and optimization
A Wiley-Interscience publication
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4.

図書

図書
by Wei Wang, Jiong Yang
出版情報: New York : Springer, c2005  xi, 160 p. ; 25 cm
シリーズ名: The Kluwer international series on advances in database systems ; 28
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5.

図書

図書
J.R. Parker
出版情報: Indianapolis, Ind. : Wiley, c2011  xxiv, 480 p. ; 24 cm
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6.

図書

図書
Bruno R. Preiss
出版情報: New York : John Wiley & Sons, 1999  xvii, 635 p. ; 25 cm
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目次情報: 続きを見る
Algorithm Analysis
Asymptotic Notation
Foundational Data Structures
Data Types and Abstraction
Stacks, Queues, and Deques
Ordered Lists and Sorted Lists
Hashing, Hash Tables, and Scatter Tables
Trees
Search Trees
Heaps and Priority Queues
Sets, Multisets, and Partitions
Garbage Collection
Algorithmic Patterns and Problem Solvers
Sorting Algorithms and Sorters
Graphs and Graph Algorithms
Appendices
Bibliography
Index
Algorithm Analysis
Asymptotic Notation
Foundational Data Structures
7.

図書

図書
Ernst W. Mayr, Hans Jürgen Prömel, Angelika Steger (eds.)
出版情報: Berlin ; Tokyo : Springer, c1998  xii, 344 p. ; 24 cm
シリーズ名: Lecture notes in computer science ; 1367
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目次情報: 続きを見る
This textbook-like tutorial is a coherent
And essentially self-contained presentation of the enormous
Recent progress facilitated by the interplay
Between the theory of probabistically checkable
Proofs and aproximation algorithms
The basic concepts, methods, and results are presented
In a unified way to provide a smooth introduction for newcomers
These lectures are particularly useful for advanced
Courses or reading groups on the topic
This textbook-like tutorial is a coherent
And essentially self-contained presentation of the enormous
Recent progress facilitated by the interplay
8.

図書

図書
Christian Scheideler
出版情報: Berlin : Springer, c1998  xvii, 234 p. ; 24 cm
シリーズ名: Lecture notes in computer science ; 1390
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9.

図書

図書
Donald E. Knuth
出版情報: Reading, Mass. ; Tokyo : Addison-Wesley, c1998  xiii, 762 p. ; 24 cm
シリーズ名: The art of computer programming ; v. 2
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目次情報: 続きを見る
Random Numbers / 3:
Introduction
Generating Uniform Random Numbers
The Linear Congruential Method
Other Methods
Statistical Tests
General Test Procedures for Studying Random Data
Empirical Tests
Theoretical Tests
The Spectral Test
Other Types of Random Quantities
Numerical Distributions
Random Sampling and Shuffling
What Is a Random Sequence?
Summary
Arithmetic / 4:
Positional Number Systems
Floating Point Arithmetic
Single-Precision Calculations
Accuracy of Floating Point Arithmetic
Double-Precision Calculations
Distribution of Floating Point Numbers
Multiple Precision Arithmetic
The Classical Algorithms
Modular Arithmetic
How Fast Can We Multiply?
Radix Conversion
Rational Arithmetic
Fractions
The Greatest Common Divisor
Analysis of Euclid's Algorithm
Factoring into Primes
Polynomial Arithmetic
Division of Polynomials
Factorization of Polynomials
Evaluation of Powers
Evaluation of Polynomials
Manipulation of Power Series
Answers to Exercises
Tables of Numerical Quantities / Appendix A:
Fundamental Constants (decimal)
Fundamental Constants (octal)
Harmonic Numbers, Bernoulli Numbers, Fibonacci Numbers
Index to Notations / Appendix B:
Index and Glossary. 0201896842T03062003
Random Numbers / 3:
Introduction
Generating Uniform Random Numbers
10.

図書

図書
Jens Knoop
出版情報: Berlin ; Tokyo : Springer, c1998  xxv, 288 p. ; 24 cm
シリーズ名: Lecture notes in computer science ; 1428
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