[데이터 사이언스] Social Network Analysis
in CS Review on DataScience
Chapter 9. Social Network Analysis
- Social Network Introduction
- Social Network Generation
- Mining on Social Network
[데이터 사이언스] Data Preprocessing
in CS Review on DataScience
Chapter 3. Data Preprocessing
- Data Preprocessing: An Overview
- Data Quality
- Major Tasks in Data Preprocessing
- Data Cleaning
- Data Integration
- Data Reduction
- Data Transformation and Data Discretization
[데이터 사이언스] Cluster Analysis
in CS Review on DataScience
Chapter 7. Cluster Analysis
- What is Cluster Analysis?
- Types of Data in Cluster Analysis
- A categorization of Major Clustering
- Partitioning Methods
- Hierarchical Methods
- Density-Based Methods
- Outlier Analysis
[데이터 사이언스] Getting to know your data
in CS Review on DataScience
Chapter 2. Getting to know your data
- Data Objects and Attribute Types
- Basic Statistical Descriptions of Data
- Data visualization
- Measuring Data Similarity and Dissimilarity
[데이터 사이언스] Recommendation Systems: Concepts, Techniques, and Applications
in CS Review on DataScience
이번 강의는 Recommendation system의 관한 내용으로 김상욱 교수님 연구실 성과를 비롯해 여러 논문들을 짧게 살펴볼 예정이다.
[데이터 사이언스] Classification & Prediction (3)
in CS Review on DataScience
Chapter 6. Classification and Prediction
- Lazy learners(or learning from your neighbors)
- Prediction
- Accuracy and error measures
- Ensemble methods
- Model selection
- Summary
[데이터 사이언스] Classification & Prediction (2)
in CS Review on DataScience
Chapter 6. Classification and Prediction
- Bayesian classification
- Rule-based classification
- Associative classification
[데이터 사이언스] Classification & Prediction (1)
in CS Review on DataScience
Chapter 6. Classification and Prediction
- What is classification & Prediction?
- Issues regarding classification and prediction
- Classification by decision tree induction
- Bayesian classification
- Rule-based classification
- Associative classification
- Lazy learners(or learning from your neighbors)
- Prediction
- Accuracy and error measures
- Ensemble methods
- Model selection
- Summary
[데이터 사이언스] Mining max patterns & closed patterns
in CS Review on DataScience
Frequent pattern이 너무 많아서 그 대신 max pattern이나 closed pattern과 같은 좀 더 엄격한 룰을 적용한 패턴을 마이닝하는 것이 보완책으로 제안되었다.
[데이터 사이언스] FP-growth vs Apriori
in CS Review on DataScience
이전에 Apriori에 대해 살펴보았는데 Apriori의 문제점을 개선하는 것들을 소개하고 특정 상황에서는 더 좋은 알고리즘인 FP Growth에 대해 알아보자.
[데이터 사이언스] Introduction
in CS Review on DataScience
Chapter 1: Introduction
- Motivation: Why data mining?
- What is data mining?
- Data Mining: On what kind of data?
- Data mining functionality
- Classification of data mining systems
- Top-10 most popular data mining algorithms
- Major issues in data mining
- Summary
[데이터 사이언스] Frequent pattern mining
in CS Review on DataScience
