このコースについて
4.3
194件の評価
41件のレビュー

次における6の4コース

100%オンライン

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約15時間で修了

英語

字幕:英語

習得するスキル

StreamsSequential Pattern MiningData Mining AlgorithmsData Mining

次における6の4コース

100%オンライン

自分のスケジュールですぐに学習を始めてください。

柔軟性のある期限

スケジュールに従って期限をリセットします。

約15時間で修了

英語

字幕:英語

シラバス - 本コースの学習内容

1
1時間で修了

Course Orientation

The course orientation will get you familiar with the course, your instructor, your classmates, and our learning environment....
1件のビデオ (合計7分), 3 readings, 1 quiz
1件のビデオ
3件の学習用教材
Syllabus10 分
About the Discussion Forums10 分
Social Media10 分
1の練習問題
Orientation Quiz10 分
4時間で修了

Module 1

Module 1 consists of two lessons. Lesson 1 covers the general concepts of pattern discovery. This includes the basic concepts of frequent patterns, closed patterns, max-patterns, and association rules. Lesson 2 covers three major approaches for mining frequent patterns. We will learn the downward closure (or Apriori) property of frequent patterns and three major categories of methods for mining frequent patterns: the Apriori algorithm, the method that explores vertical data format, and the pattern-growth approach. We will also discuss how to directly mine the set of closed patterns....
9件のビデオ (合計49分), 2 readings, 3 quizzes
9件のビデオ
1.2. Frequent Patterns and Association Rules5 分
1.3. Compressed Representation: Closed Patterns and Max-Patterns7 分
2.1. The Downward Closure Property of Frequent Patterns3 分
2.2. The Apriori Algorithm6 分
2.3. Extensions or Improvements of Apriori7 分
2.4. Mining Frequent Patterns by Exploring Vertical Data Format3 分
2.5. FPGrowth: A Pattern Growth Approach8 分
2.6. Mining Closed Patterns3 分
2件の学習用教材
Lesson 1 Overview10 分
Lesson 2 Overview10 分
2の練習問題
Lesson 1 Quiz10 分
Lesson 2 Quiz8 分
2
1時間で修了

Module 2

Module 2 covers two lessons: Lessons 3 and 4. In Lesson 3, we discuss pattern evaluation and learn what kind of interesting measures should be used in pattern analysis. We show that the support-confidence framework is inadequate for pattern evaluation, and even the popularly used lift and chi-square measures may not be good under certain situations. We introduce the concept of null-invariance and introduce a new null-invariant measure for pattern evaluation. In Lesson 4, we examine the issues on mining a diverse spectrum of patterns. We learn the concepts of and mining methods for multiple-level associations, multi-dimensional associations, quantitative associations, negative correlations, compressed patterns, and redundancy-aware patterns....
9件のビデオ (合計47分), 2 readings, 2 quizzes
9件のビデオ
3.2. Interestingness Measures: Lift and χ25 分
3.3. Null Invariance Measures5 分
3.4. Comparison of Null-Invariant Measures7 分
4.1. Mining Multi-Level Associations4 分
4.2. Mining Multi-Dimensional Associations2 分
4.3. Mining Quantitative Associations4 分
4.4. Mining Negative Correlations6 分
4.5. Mining Compressed Patterns7 分
2件の学習用教材
Lesson 3 Overview10 分
Lesson 4 Overview10 分
2の練習問題
Lesson 3 Quiz10 分
Lesson 4 Quiz8 分
3
2時間で修了

Module 3

Module 3 consists of two lessons: Lessons 5 and 6. In Lesson 5, we discuss mining sequential patterns. We will learn several popular and efficient sequential pattern mining methods, including an Apriori-based sequential pattern mining method, GSP; a vertical data format-based sequential pattern method, SPADE; and a pattern-growth-based sequential pattern mining method, PrefixSpan. We will also learn how to directly mine closed sequential patterns. In Lesson 6, we will study concepts and methods for mining spatiotemporal and trajectory patterns as one kind of pattern mining applications. We will introduce a few popular kinds of patterns and their mining methods, including mining spatial associations, mining spatial colocation patterns, mining and aggregating patterns over multiple trajectories, mining semantics-rich movement patterns, and mining periodic movement patterns....
10件のビデオ (合計56分), 2 readings, 2 quizzes
10件のビデオ
5.2. GSP: Apriori-Based Sequential Pattern Mining3 分
5.3. SPADE—Sequential Pattern Mining in Vertical Data Format3 分
5.4. PrefixSpan—Sequential Pattern Mining by Pattern-Growth4 分
5.5. CloSpan—Mining Closed Sequential Patterns3 分
6.1. Mining Spatial Associations4 分
6.2. Mining Spatial Colocation Patterns9 分
6.3. Mining and Aggregating Patterns over Multiple Trajectories9 分
6.4. Mining Semantics-Rich Movement Patterns3 分
6.5. Mining Periodic Movement Patterns7 分
2件の学習用教材
Lesson 5 Overview10 分
Lesson 6 Overview10 分
2の練習問題
Lesson 5 Quiz10 分
Lesson 6 Quiz8 分
4
5時間で修了

Week 4

Module 4 consists of two lessons: Lessons 7 and 8. In Lesson 7, we study mining quality phrases from text data as the second kind of pattern mining application. We will mainly introduce two newer methods for phrase mining: ToPMine and SegPhrase, and show frequent pattern mining may be an important role for mining quality phrases in massive text data. In Lesson 8, we will learn several advanced topics on pattern discovery, including mining frequent patterns in data streams, pattern discovery for software bug mining, pattern discovery for image analysis, and pattern discovery and society: privacy-preserving pattern mining. Finally, we look forward to the future of pattern mining research and application exploration....
9件のビデオ (合計98分), 2 readings, 3 quizzes
9件のビデオ
7.2. Previous Phrase Mining Methods10 分
7.3. ToPMine: Phrase Mining without Training Data12 分
7.4. SegPhrase: Phrase Mining with Tiny Training Sets14 分
8.1. Frequent Pattern Mining in Data Streams19 分
8.2. Pattern Discovery for Software Bug Mining12 分
8.3. Pattern Discovery for Image Analysis6 分
8.4. Advanced Topics on Pattern Discovery: Pattern Mining and Society—Privacy Issue13 分
8.5. Advanced Topics on Pattern Discovery: Looking Forward4 分
2件の学習用教材
Lesson 7 Overview10 分
Lesson 8 Overview10 分
2の練習問題
Lesson 7 Quiz8 分
Lesson 8 Quiz8 分
4.3
41件のレビューChevron Right

人気のレビュー

by DDSep 10th 2017

The first several chapters are very impressive. The last three lessons are a little difficult for first-learners. The illustration are clear and easy to understand.

by GLJan 18th 2018

Excellent course. Now I have a big picture about pattern discovery and understand some popular algorithm. Also professor points out the direction for further study.

講師

Avatar

Jiawei Han

Abel Bliss Professor
Department of Computer Science

修士号の取得を目指しましょう

この コース は イリノイ大学アーバナ・シャンペーン校(University of Illinois at Urbana-Champaign) の100%オンラインの Master in Computer Science の一部です。 プログラムのすべてで認定されれば、それらのコースが学位学習に加算されます。

イリノイ大学アーバナ・シャンペーン校(University of Illinois at Urbana-Champaign)について

The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs. ...

データマイニング の専門講座について

The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp. Courses 2 - 5 of this Specialization form the lecture component of courses in the online Master of Computer Science Degree in Data Science. You can apply to the degree program either before or after you begin the Specialization....
データマイニング

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