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 pattern-based classification 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.
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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.
- 5 stars64.76%
- 4 stars24.72%
- 3 stars6.25%
- 2 stars2.47%
- 1 star1.77%
데이터 시각화의 최상위 리뷰
This very interesting course have sharpened my ability to read and interpret graphs in general and more importantly to pay more attention to every little details.
One of the excellent courses I have ever studied. Professor style of teaching is very soft and simple, point to point and very clear. I have given 100 out 100 marks.
Thank you for this amazing course, for.me the most enjoyable and amazing tool for this course is how encouraging me to find real life data repository and learn how to visualize it.
It was a very enriching experience. Coursera is such a nice platform for learners. Very good lectures. I am thankful for the team and the Instructor.
데이터 마이닝 특화 과정 정보
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.
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