Principles of data mining /
This book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clus...
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
London :
Springer,
[2020]
|
| Edition: | Fourth edition |
| Series: | Undergraduate topics in computer science
|
| Subjects: |
Table of Contents:
- Introduction to data mining
- Data for data mining
- Introduction to classification : naïve Bayes and nearest neighbour
- Using decision trees for classification
- Decision tree induction : using entropy for attribute selection
- Decision tree induction : using frequency tables for attribute selection
- Estimating the predictive accuracy of a classifier
- Continuous attributes
- Avoiding overfitting of decision trees
- More about entropy
- Inducing modular rules for classification
- Measuring the performance of a classifier
- Dearling with large volumes of data
- Ensemble classification
- Comparing classifiers
- Association rule mining I
- Association rule mining II
- Association rule mining III : frequent pattern trees
- Clustering
- Text mining
- Classifying streaming data
- Classifying streaming data II : time-dependent data
- An introduction to neural networks
- Introduction to data mining
- Data for data mining
- Introduction to classification : naïve Bayes and nearest neighbour
- Using decision trees for classification
- Decision tree induction : using entropy for attribute selection
- Decision tree induction : using frequency tables for attribute selection
- Estimating the predictive accuracy of a classifier
- Continuous attributes
- Avoiding overfitting of decision trees
- More about entropy
- Inducing modular rules for classification
- Measuring the performance of a classifier
- Dearling with large volumes of data
- Ensemble classification
- Comparing classifiers
- Association rule mining I
- Association rule mining II
- Association rule mining III : frequent pattern trees
- Clustering
- Text mining
- Classifying streaming data
- Classifying streaming data II : time-dependent data
- An introduction to neural networks