MindMap Gallery data mining
The following summarizes the knowledge content of data mining, including introduction to data mining, data preprocessing, Bayesian theory, decision tree classifier, neural network, and support vector machine.
Edited at 2021-12-20 22:24:31Avatar 3 centers on the Sully family, showcasing the internal rift caused by the sacrifice of their eldest son, and their alliance with other tribes on Pandora against the external conflict of the Ashbringers, who adhere to the philosophy of fire and are allied with humans. It explores the grand themes of family, faith, and survival.
This article discusses the Easter eggs and homages in Zootopia 2 that you may have discovered. The main content includes: character and archetype Easter eggs, cinematic universe crossover Easter eggs, animal ecology and behavior references, symbol and metaphor Easter eggs, social satire and brand allusions, and emotional storylines and sequel foreshadowing.
[Zootopia Character Relationship Chart] The idealistic rabbit police officer Judy and the cynical fox conman Nick form a charmingly contrasting duo, rising from street hustlers to become Zootopia police officers!
Avatar 3 centers on the Sully family, showcasing the internal rift caused by the sacrifice of their eldest son, and their alliance with other tribes on Pandora against the external conflict of the Ashbringers, who adhere to the philosophy of fire and are allied with humans. It explores the grand themes of family, faith, and survival.
This article discusses the Easter eggs and homages in Zootopia 2 that you may have discovered. The main content includes: character and archetype Easter eggs, cinematic universe crossover Easter eggs, animal ecology and behavior references, symbol and metaphor Easter eggs, social satire and brand allusions, and emotional storylines and sequel foreshadowing.
[Zootopia Character Relationship Chart] The idealistic rabbit police officer Judy and the cynical fox conman Nick form a charmingly contrasting duo, rising from street hustlers to become Zootopia police officers!
data mining
CH1 Introduction to Data Mining
Data mining, machine learning and artificial intelligence
Machine learning framework
Algorithm model design choices
Training set
test set
Machine learning scenarios
supervised learning
semi-supervised learning
transfer learning
unsupervised learning
reinforcement learning
Machine learning tasks
return
Classification
structure learning
machine learning algorithm
Linear
nonlinear
deep learning
Support Vector Machines
decision tree
K nearest neighbor KNN
Introduction to learning materials and data mining
data
data attributes
Qualitative attributes and quantitative attributes
Discrete and continuous attributes
Data storage and issues
storage
physical type
logical type
Data preprocessing
data integration
Data cleaning
Data curation
data conversion
Common problems and methods in data mining
data mining technology
Classification
confusion matrix
ROC curve
ROC curve drawing
ROC curve application
AUC value
clustering
distance measure
algorithm
application
Association rules
return
Data preprocessing (pre)
Data cleaning
Data Integration
data conversion
data reduction
CH2 data preprocessing
Missing, outlier, duplicate
Handle missing data
outliers
local outlier factor
Data cleaning
data conversion
Data description
Feature selection
Feature extraction
Data conversion and description
data conversion
Property category
type conversion
sampling
Imbalanced data set
upsample
edge sampling
standardization
Data description
Basic description
Correlation coefficient
Pearson product-moment correlation coefficient
Pearson chi-square test
Feature selection and feature extraction
entropy
amount of information
information gain
Principal Component Analysis PCA
Linear discriminant analysis LDA
CH3 Bayesian Theory & Decision Tree Classifier
Naive Bayes
Bayesian theory
Example: Checking for Cancer
Naive Bayes classifier
conditionally independent
Laplacian smoothing
decision tree
ID3
entropy
Attribute selection
ID3 framework
Decision tree pruning classification
Entropy deviation
CART decision tree
CART classification tree
Gini index
CART regression tree
optimal split point
pruning
CH4 Neural Network
perceptron
For linearly separable data sets
AND or gate
gradient descent method
Loss function derivation
Least squares loss function
NAND NAND gate
Other linear models
linear regression
Empirical error
Parameter calculation
regular term
Logistic function
Logistic curve
Logistic distribution
logistic regression
likelihood function
maximum likelihood estimation
Cross entropy & KL divergence
Multi-classification problem
Softmax returns
multilayer perceptron
CNN-RNN-Transformers
CH5 support vector machine