MindMap Gallery Computer-Data Mining Course
This is a mind map about data mining courses, including basic classification methods, advanced classification methods, basic clustering methods, etc.
Edited at 2023-11-24 19:46:19Avatar 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
Classification
Basic classification method
decision tree
type
ID3: Based on information gain
C4.5: Based on information gain rate
CART: based on Gini index
Common algorithms
Rainforest
Build trees based on AVC-list
BOAT
Resampling with replacement
Naive Bayes
Premise: Each attribute is independent of each other
Rule-based classification
IF-THEN rules
Extraction rules
Extract directly from the decision tree
Extracting from samples: sequential coverage method
Model evaluation and selection
Evaluate
confusion matrix
Accuracy, error rate, sensitivity, specificity, precision, recall, F-measure
Holdout method, cross validation, Bootstrap
choose
hypothetical test
ROC curve
Improve classification accuracy
Bagging
Boosting
random forest
Advanced classification methods
Bayesian belief network
Directed acyclic graph conditional probability table
Artificial neural networks
Backpropagation algorithm
Support vector machineSVM
Linear SVM
Nonlinear SVM
Classification based on frequent patterns
Lazy Learning: KNN, CBR (Case-Based Reasoning)
Other methods
Genetic Algorithm CA
semi-supervised learning methods
Self-training Collaborative training
transfer learning
clustering
Basic clustering methods
Division method
k-means
k-center
PAM
Improvements to PAM
CLARA, CLARANS
hierarchical approach
condense
BIRHC (incremental clustering)
Chameleon (dynamic model)
Split
Density-based clustering
DBSCAN
Clustering evaluation methods
Estimate clustering trends
Hopkins statistic
Determine the number of clusters
empirical method
ELBOW METHOD
m times cross-confirmation
Determine clustering quality
external method
BCubed
intrinsic method
Contour coefficient
Advanced clustering methods