No programming experience required.
Data mining is the process to discover interesting knowledge from large amounts of data. It is an interdisciplinary field with contributions from many areas, such as statistics, machine learning, information retrieval, pattern recognition and bioinformatics. Data mining is widely used in many domains, such as retail, finance, telecommunication and social media.
The main techniques for data mining include classification and prediction, clustering, outlier detection, association rules, sequence analysis, time series analysis and text mining, and also some new techniques such as social network analysis and sentiment analysis.No programming experience required.
Data mining is the process to discover interesting knowledge from large amounts of data. It is an interdisciplinary field with contributions from many areas, such as statistics, machine learning, information retrieval, pattern recognition and bioinformatics. Data mining is widely used in many domains, such as retail, finance, telecommunication and social media.
The main techniques for data mining include classification and prediction, clustering, outlier detection, association rules, sequence analysis, time series analysis and text mining, and also some new techniques such as social network analysis and sentiment analysis.
R and Data Mining: Examples and Case Studies
Published by Elsevier
Yanchang Zhao
rand data mining: examples and case studies
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| Homeworks and Projects.Homeworks and Projects. Homework San’s projects. |
40 | |
| Midterm examMidterm exam midterm exam |
30 | |
| Final examFinal exam final exam |
30 |
