Welcome to Data Mining Blog

The Data Mining course introduces the concepts and methods of data mining and shows its relationship with data science. All the steps involved in knowledge data discovery will be discussed. Topics include Introduction to Data Mining, Data Preparation and Pre-processing, Classification, Model Evaluation & Selection, Clustering, Association Analysis, and ends with Future Trends & Challenges. The algorithm for each modelling process is discussed with supporting examples using real-world datasets. These datasets are used for model building using assessable technology with easy-to-use platforms. The findings will be presented through digital tools. The knowledge and practical skills gained from this course would benefit the students for solving real problems in industry or society-related issues for various SDG-based applications.

At the end of the course, students should be able to: 

1. Assess the methods of data mining related to real application in data science ( C5  )

2. Manipulate data mining methods based on the given tasks using data mining tools ( P4  )

3. Organize the use of digital skills in model development related to the data mining project ( P5  )

Assessment

Continuous Assessment: 60.00% 

- Group Project - 30%

- Test (Lab Test1, Lab Test2) - 30%

Final Assessment: 40.00%

Recommended Text

Shuzlina Abdul Rahman & Sofianita Mutalib, Predictive Analytics Applications with WEKA, n/a, 2021, ISBN: 9672355033

Pang-Ning Tan,Michael Steinbach,Anuj Karpatne,Vipin Kumar, Introduction to Data Mining, Addison-Wesley, 2019, ISBN: 9780133128901

References

 Mohammed J. Zaki,Wagner Meira, Jr, Data Mining and Machine Learning, Cambridge University Press, 2020, ISBN:

 9781108473989

 Davy Cielen,Arno Meysman,Mohamed Ali, Introducing Data Science, Manning Publications, 2016, ISBN:

 9781633430037

 Galit Shmueli,Peter C. Bruce,Inbal Yahav,Nitin R. Patel,Kenneth C. Lichtendahl, Jr., Data Mining for Business

 Analytics, John Wiley & Sons, 2017, ISBN: 9781118879368

 Sebastian Raschka,Vahid Mirjalili, Python machine learning : machine learning and deep learning with Python,

 scikit-learn, and TensorFlow, n/a, 2017, ISBN: 9781787125933

 Jiawei Han, Micheline Kamber and Jian Pei, Data Mining: Concepts and Techniques, 3, Morgan Kaufmann Publishers,

 2014, ISBN: 9780123814791

 Ian H. Witten,Eibe Frank,Mark A. Hall,Christopher J. Pal, Data Mining: Practical Machine Learning Tools and

 Techniques, Morgan Kaufmann, 2011, ISBN: 978012804291