Über diesen Kurs
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Ca. 15 Stunden zum Abschließen

Englisch

Untertitel: Englisch

Kompetenzen, die Sie erwerben

StreamsSequential Pattern MiningData Mining AlgorithmsData Mining

100 % online

Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.

Flexible Fristen

Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.

Ca. 15 Stunden zum Abschließen

Englisch

Untertitel: Englisch

Lehrplan - Was Sie in diesem Kurs lernen werden

Woche
1
1 Stunde zum Abschließen

Course Orientation

The course orientation will get you familiar with the course, your instructor, your classmates, and our learning environment.

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1 Video (Gesamt 7 min), 3 Lektüren, 1 Quiz
1 Video
3 Lektüren
Syllabus10m
About the Discussion Forums10m
Social Media10m
1 praktische Übung
Orientation Quiz10m
4 Stunden zum Abschließen

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.

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9 Videos (Gesamt 49 min), 2 Lektüren, 3 Quiz
9 Videos
1.2. Frequent Patterns and Association Rules5m
1.3. Compressed Representation: Closed Patterns and Max-Patterns7m
2.1. The Downward Closure Property of Frequent Patterns3m
2.2. The Apriori Algorithm6m
2.3. Extensions or Improvements of Apriori7m
2.4. Mining Frequent Patterns by Exploring Vertical Data Format3m
2.5. FPGrowth: A Pattern Growth Approach8m
2.6. Mining Closed Patterns3m
2 Lektüren
Lesson 1 Overview10m
Lesson 2 Overview10m
2 praktische Übungen
Lesson 1 Quiz10m
Lesson 2 Quiz8m
Woche
2
1 Stunde zum Abschließen

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.

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9 Videos (Gesamt 47 min), 2 Lektüren, 2 Quiz
9 Videos
3.2. Interestingness Measures: Lift and χ25m
3.3. Null Invariance Measures5m
3.4. Comparison of Null-Invariant Measures7m
4.1. Mining Multi-Level Associations4m
4.2. Mining Multi-Dimensional Associations2m
4.3. Mining Quantitative Associations4m
4.4. Mining Negative Correlations6m
4.5. Mining Compressed Patterns7m
2 Lektüren
Lesson 3 Overview10m
Lesson 4 Overview10m
2 praktische Übungen
Lesson 3 Quiz10m
Lesson 4 Quiz8m
Woche
3
2 Stunden zum Abschließen

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.

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10 Videos (Gesamt 56 min), 2 Lektüren, 2 Quiz
10 Videos
5.2. GSP: Apriori-Based Sequential Pattern Mining3m
5.3. SPADE—Sequential Pattern Mining in Vertical Data Format3m
5.4. PrefixSpan—Sequential Pattern Mining by Pattern-Growth4m
5.5. CloSpan—Mining Closed Sequential Patterns3m
6.1. Mining Spatial Associations4m
6.2. Mining Spatial Colocation Patterns9m
6.3. Mining and Aggregating Patterns over Multiple Trajectories9m
6.4. Mining Semantics-Rich Movement Patterns3m
6.5. Mining Periodic Movement Patterns7m
2 Lektüren
Lesson 5 Overview10m
Lesson 6 Overview10m
2 praktische Übungen
Lesson 5 Quiz10m
Lesson 6 Quiz8m
Woche
4
5 Stunden zum Abschließen

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.

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9 Videos (Gesamt 98 min), 2 Lektüren, 3 Quiz
9 Videos
7.2. Previous Phrase Mining Methods10m
7.3. ToPMine: Phrase Mining without Training Data12m
7.4. SegPhrase: Phrase Mining with Tiny Training Sets14m
8.1. Frequent Pattern Mining in Data Streams19m
8.2. Pattern Discovery for Software Bug Mining12m
8.3. Pattern Discovery for Image Analysis6m
8.4. Advanced Topics on Pattern Discovery: Pattern Mining and Society—Privacy Issue13m
8.5. Advanced Topics on Pattern Discovery: Looking Forward4m
2 Lektüren
Lesson 7 Overview10m
Lesson 8 Overview10m
2 praktische Übungen
Lesson 7 Quiz8m
Lesson 8 Quiz8m
4.2
42 BewertungenChevron Right

Top reviews from Pattern Discovery in Data Mining

von 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.

von 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.

Dozent

Avatar

Jiawei Han

Abel Bliss Professor
Department of Computer Science

Beginnen Sie damit, auf Ihren Master-Abschluss hinzuarbeiten.

This Kurs is part of the 100% online Master in Computer Science from University of Illinois at Urbana-Champaign. If you are admitted to the full program, your courses count towards your degree learning.

Über University of Illinois at Urbana-Champaign

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. ...

Über die Spezialisierung Data-Mining

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....
Data-Mining

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