Pattern Discovery in Data Mining

University of Illinois at Urbana-Champaign via Coursera

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Details

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Provider

Coursera
Pricing

Free Online Course (Audit)
Languages

English
Certificate

Paid Certificate Available
Duration & workload

17 hours 17 minutes
Sessions

On-Demand
Subtitles

Arabic, French, Portuguese, Italian, German, Russian, English, Spanish, Korean, Thai, Indonesian, Kazakh, Hindi, Swedish, Greek, Chinese, Ukrainian, Japanese, Polish, Dutch, Turkish, Hungarian, Bengali, Pashto, Urdu, Azerbaijani, Farsi

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Overview

Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern discovery in data mining. We will also introduce methods for data-driven phrase mining and some interesting applications of pattern discovery. This course provides you the opportunity to learn skills and content to practice and engage in scalable pattern discovery methods on massive transactional data, discuss pattern evaluation measures, and study methods for mining diverse kinds of patterns, sequential patterns, and sub-graph patterns.

Syllabus

Course Orientation

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

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.

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.

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.

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.

Taught by

Jiawei Han

Reviews

2.1 rating, based on 21 Class Central reviews

4.3 rating at Coursera based on 319 ratings

Start your review of Pattern Discovery in Data Mining

Gregory J Hamel ( Life Is Study) @greg

Pattern discovery in data mining is the first course in a new 5-part data mining specialization offered by the University of Illinois at Urbana-Champaign through Coursera. Keeping with the trend of other specialization courses, pattern discovery in…

Pattern discovery in data mining is the first course in a new 5-part data mining specialization offered by the University of Illinois at Urbana-Champaign through Coursera. Keeping with the trend of other specialization courses, pattern discovery in data mining spans 4 weeks and will likely be offered again each month or two after the first offering. The course covers a range of methods for finding different types of patterns in data, such as association rules and patterns in graphs. Grading is based exclusively on 4 weekly quizzes.

I was excited to see the new data mining specialization come up on Coursera to kick off 2015, but unfortunately, pattern discovery in data mining is a dull, poorly executed information dump. Besides an interesting topic, there’s not much going for this course. In the lectures, the professor reads information off dense slides and his delivery is more confusing than instructive. The slides, video and sound are of decent quality, but the explanations are not clear and while I normally don't have an issue with foreign accents, the professor's English made things harder to understand. To make things worse, there are few instructive in lecture quizzes and no activities or programming assignments. A course about data mining should have programming assignments or activities that let students interact with the concepts to reinforce learning.

Pattern discovery in data mining is a disappointing start to the data mining specialization, that suffers from poor instruction quality and lack of illustrative assignments. Taking this course is like a data mining problem in and of itself: you have to spend a lot of time deciphering the lectures to uncover useful information.

I give this course 2 out of 5 stars: Poor.
Bence Komarniczky

I have finished watching the first week's worth of lectures and so far this course is a big disappointment. There are very few notes on actual implementation of the methods that are only mentioned briefly. The complete lack of programming assignment…

I have finished watching the first week's worth of lectures and so far this course is a big disappointment.
There are very few notes on actual implementation of the methods that are only mentioned briefly.
The complete lack of programming assignments is also a big no-no for me in such a course.
If that wasn't enough you have to pay $40 for a certificate as the course offers no unverified certificates, so if you want any paper for finishing you have to dish out $40. Which I think is too much, considering there were about 1.5 hours worth of lectures for the first week.

This is my 3rd course from Illinois and the other 2 were superb (Heterogeneous Parallel Programming, Introduction to Macroeconomics) but this one feels very rough and unfinished. They should at least give the option of unverified certificates.
Anonymous

Bence is pretty on point with his review. IMO, this seems like they are just trying to milk some cash out of this whole platform/system. By having no unverified certificates, there will be people willing to pay $40 for the certificate. Also, durin…

Bence is pretty on point with his review. IMO, this seems like they are just trying to milk some cash out of this whole platform/system.

By having no unverified certificates, there will be people willing to pay $40 for the certificate. Also, during the first week's worth of lectures, it seems that Jiawei Han is encouraging you to buy his books.

This is a Coursera class for personal growth/development. I don't see any reason why anyone should buy a college level textbook unless you are truly interested at the end of it and want to carry on further with this topic.

Moving on to the actual content, the lectures aren't too terrible, but I feel that I could have read a wikipedia article and obtained as much knowledge from it.

Maybe it will get better, but at this point, not impressed.
Anonymous

Horrible Course with no practical component. A lot of theory with not even one practical example. Please avoid! This course is not worth it and you can get all the knowledge from wikipedia
Anonymous

This course is very disappointing due to the poor quality of the video lectures as well as the frequent errors present in the example datasets and even the quizzes. After the second week I stopped watching the videos completely because they were not worth the time and instead only read the textbook.

This course has a lot of breadth but very little depth. The topics are rushed through and explained very briefly with no explanation as to why certain methods are useful or how to apply them to actual data. The course length is also too short for the amount of information that is attempted to be conveyed, 6 weeks would be more appropriate.
Anonymous

The course content is definitely not the type of material to be learned from watching a video. The lecturer simply gives the theory with very basic examples and only brushes over the concepts. Not the greatest method of teaching.

If there were programming assignments, the class could be much more useful for people who are looking to learn about the practical applications of the topic. I am taking the class simply because I'm a data science specialist and want the data mining specialization certificate. I am already familiar with the topic and I find the lecture lacking.
Anonymous

Ditto on everything said about the lack of programming assignments and applicability. Took the class and got 100% on every quiz. If you asked me to apply any of it I'd be in the deep end of the pool. Very disappointed because the professor is extremely accomplished, UI has a fantastic rep, and the students the are smart, active participants. But in my estimation this class really detracts from what Coursera is/should be trying to accomplish from both an educational and certification perspective.
Apoorv Vyas

I have finished taking 3 week lectures. I will have to say that the course did not meet my expectations.
- Lacks Practical Implementation aspects
- Lacks Clarity in many places.

The course will make you informed about the options you have to deal with various types of problems. You will have to go and read papers to get the details.

For someone who is looking for something more practical I would recommend Mining Massive Datasets Course.
Edwin De Jong

Unfortunately, the course has not kept up with my expectations. There are no practical assignments and no details on implementation of the various methods. I really hope the next course from this specialization will be more engaging.
Anonymous

I too think it lacked a practical, programming part. Week 1 and 2 were interesting but by the last week it seemed to be more focused on mentioning as many algorithms as fast as possible. This is a bit of a wasted opportunity
Pandis Charalambos

Weeks 1 and 2 where good. Unfortunately weeks 3 and 4 where not. The instructor tried to mention as many algorithms as he could without describing them efficiently. Also the instructor should try to improove his english because many of us are intern…

Weeks 1 and 2 where good. Unfortunately weeks 3 and 4 where not. The instructor tried to mention as many algorithms as he could without describing them efficiently. Also the instructor should try to improove his english because many of us are international students and I personaly found it difficult to fully understand him sometimes.
Finally the instructor claimed that his lectures are enought to fully understand the principles he described and further reading is optional. This was not true. The instructor should make longer lectures if he want to keep the same teaching material, or reduce the teaching material and keep the same lenght of lectures.
In conclusion the course is very interesting but can be (and should be) improved.
Kristina Šekrst

I liked the way I was able to learn more about the newest trends in pattern discovery, but, as with other courses, there was too much theory, and too little practice. However, it was a fun experience, but I hope in the second iteration that the ratio of the programming assignments and the theoretical descriptions of various algorithms and papers will be equal.
Anonymous

Quite boring. I agree that subject is dry itself but instructor needs to be a great communicator (IMO). Moving on to other courses at Coursera
Woof

No real example of data or programming application. Only concepts flowing around in the course lecture. Not useful at all.
Deepak Jois
Anonymous

I'm enjoying learning the different types of situations where we can mine patterns - transactions, sequences, graphs. The instructor's not spoon-feeding anyone, but the quizzes aren't bad if you pay attention and read the forums. There's been some technical issue with quiz answers and stuff, but the Coursera team has always fixed them well before the due dates.
Peter

This is a very good theoretical and algorithmic course on pattern discovery. It gives you a pretty good idea what is going on the pattern discovery field with comprehensive lists of current research papers. I feel learned a lot from this short 4 weeks course. Yes, this is no programming assignment. The theory and algorithms are already complicate enough. It's not practical or useful to add programming into this short course.
Anonymous
Colin Khein
Mark Henry Butler