資料探勘研究與實務

The main objective of this class is to study techniques and applications of data mining, drawing work from areas including database technology, artificial intelligence, and knowledge-based systems. This course will cover Data Warehousing and Data Mining, as well as some topics related to Text Mining and Recommender Systems. Students are required to accomplish project assignments on the implementation and experiment on mining data from various application domains.

無

無備註

教師未提供此項資料

Homework & Participation (15%), Project & Presentation (25%), Midterm (30%), Final (30%)

• Introduction

   Overview

  講授：

  3

• Data Preprocessing

   Data cleaning  Data transformation  Data reduction  Data discretization

  講授：

  3

• Mining Association Rules

   Mining frequent patterns  Apriori algorithm  Multidimensional association rules

  講授：

  5

• Classification

   Decision tree  Bayesian classification  Rule-based classification  Neural network  Support vector machines  K-NN classifiers  Genetic algorithm  Accuracy, Precision, Recall

  講授：

  9

• Cluster Analysis

   K-means  Hierarchical clustering  Expectation-maximization  Self-organizing Maps

  講授：

  6

• Text Mining

   Basic concepts  Information retrieval  Vector Space Model  Language Model

  講授：

  6

• Recommender Systems

   Content-based approach  Collaborative-filtering  Hybrid approach

  講授：

  6

• Link Analysis and Social Network Analysis

   Social Network Analysis  HITS, PageRank  Ontology-based network analysis  Trust Netwrok: prediction & propagation

  講授：

  5

• Data Warehousing

   Data Warehousing  Multidimensional data model  Data warehouse architecture  OLAP Technology

  講授：

  5

Data Mining: Concepts and Techniques, 3rd ed., Morgan Kaufmann Publishers, 2011, by Jiawei Han, Micheline Kamber and Jian Pei; Course slides: http://www-faculty.cs.uiuc.edu/~hanj/bk3/

地點

MB 305

時間

Fri. pm 12:30 ~ 1:30

聯絡方式

dliu@iim.nctu.edu.tw