Data mining

Learning Results

The aim of the course is to present Data Mining techniques, as well as their applications. It will provide and introduction to the field of analytics, and therefore the extensive use of data, statistical and quantitative analysis, exploratory and predictive models to mine and discover unexpected but ueful glimpses of previously unkown information. We discuss standard data mining algorithms that can be applied both on structured and unstructured data and experience their impact on decision making situations.

Upon successful completion of the course students:

• will acquire knowledge of the principles, procedures and applications of the scientific field of Data Mining.
• will delved into the methods and algorithms of data mining and acquire the appropriate skills to implement these algorithms, as well as the practical experience, having become familiar with specialized software packages.
• will study standard cases of data mining algorithms to real problems, in order to acquire specialized problem-solving skills, which are required in research and/or innovation in order to develop new knowledge and processes, especially in multidisciplinary fields,
• will acquire the necessary learning skills that will allow them to continue their studies in the field of Data Mining in an autonomous fashion, to a large extent.

Skills acquired

• Search, analysis and synthesis of data and information, using the necessary technologies
• Individual work
• Group work
• Work in an multi-disciplinary environment
• Production of new research ideas
• Creative and critical thinking

• Introduction – Basic concepts of Data Mining
• Data quality
• Data pre-processing
• Association rule mining
• Classification
  • basic concepts and algorithms
  • decodom trees
  • Model evaluation
  • Alternative classification/predictions algorithms: Naïve Bayes, Neural Networks
• Clustering
  • Partitional Algorithms
  • Hierarchical Algorithms
  • Clustering Categorical Data

• Text Mining
• Case studies of Data Mining Algorithms (throughout the semester)

1. J. Han, M. Kamber, J. Pei, Data Mining: Concepts and Techniques, 3rd ed., Morgan Kaufmann, 2011.

2. P. Tan, M. Steinbach, V. Kumar, Introduction to Data Mining, Pearson, 2018, 2nd edition.

3. M. Zaki, W. Meira Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, 2017.

4. I. Witten, E. Frank, Μ. Hall, Data Mining: Practical Machine Learning Tools and Techniques, 3rd ed., Morgan Kaufmann, 2011.

Related scientific journals:

1. Data Mining and Knowledge Discovery, Springer.

2. IEEE Transactions on Knowledge and Data Engineering, IEEE.

3. ACM Transactions on Knowledge Discovery from Data, ACM.

4. SIGKDD Explorations, ACM.

5. Intelligent Data Analysis, IOS Press.

6. IEEE Transactions on Neural Networks and Learning Systems, IEEE