Data Mining

한국과학기술원
이도헌

페이스북 트위터 카카오톡 네이버밴드 링크복사

주제분류

공학 >컴퓨터ㆍ통신 >컴퓨터과학
등록일자

2010.09.10

조회수

6,935

This course addresses essential data mining techniques such as clustering, classification, association, and network inference, and explains their applications to various bioinformatics problems including gene analysis, disease diagnosis, and biosystem reverse engineering.

차시별 강의

1.	Overview	1. Definitions of and differences between data mining and machine learning 2. Types of data mining techniques
2.	Simple classification	1. Understanding simple classification techniques including Naive Bayesian Classifier
3.	Decision tree	1. Understanding the rational of decision tree construction algorithms
4.	Decision tree 2	1. How to handle missing values during decision tree making 2. The importance of pruning and the strategy 3. Examples of decision tree in Bioinformatics 4. Pros and Cons of decision tree
5.	Association Rule	1. Understanding the definition of association rule 2. Apriori algorithm for mining association rules 3. Examples of association rules in Bioinformatics
6.	Clustering 1	1. Understanding the concept of clustering 2. Various ways to represent s and distances between s
7.	Clustering 2	1. Understanding Hierarchical clustering, K-means clustering, Self-organizing map 2. Different ways to validate clusters
8.	Artificial Neural Network	1. Understanding the concept of artificial neural network 2. Basing building blocks of artificial neural network 3. Multi- perceptrons with backpropagation
9.	PCA and LDA	1. Understanding the concepts of principle component analysis and linear discriminant analysis
10.	SVM	1. Understanding the concept of support vector machine 2. Optimal hyperplane and how to acquire optimal hyperplane
11.	Fuzzy set and Fuzzy logic	1. Fuzzy sets and set operations 2. Extension principle 3. Fuzzy logic and inference 4. Fuzzy measure
12.	Credibility analysis	1. Understanding the important measures for analyzing the credibility 2. Error types in binary classification 3. Meaning of ROC curve 4. How to calculate p value in multiple testing
13.	Data preparation and reduction	1. Understanding the data preparation procedures including normalization and handling missing values 2. Understanding the importance of data reduction, example of feature selection