| Symbolic data analysis | |
| Course Leader | Rosanna Verde |
| Target Group | Staff involved in preparing quality reports |
| Entry Qualifications | - Sound command of English. Participants should be able to make short interventions and to actively participate in discussions
- A good knowledge of quality management is a prerequisite for attending the course.
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| Objective(s) | - Gain a comprehensive understanding of Symbolic Data Analysis as conceived within Benzecri’s French school of "analyse des données," appreciating its historical and conceptual foundations.
- Develop expertise in distinguishing and managing various forms of symbolic data, enhancing their versatility in working with diverse datasets.
- Acquire hands-on skills to preprocess symbolic data efficiently, preparing it effectively for subsequent analysis.
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| Contents | In the recent years symbolic data analysis (SDA) has assumed an important role in the framework of Benzecri’s French school of analyse des données, developing standard exploratory techniques of multivariate data analysis for the study of more general and complex data, called symbolic data. The course will focus on: Day 1 - Introduction to symbolic data analysis
- Symbolic data types
- Data preprocessing for symbolic data
- Basic symbolic data analysis techniques
Day 2 - Advanced symbolic data analysis techniques
- Clustering of symbolic data
- Classification of symbolic data
- Association rule mining
Day 3 - Applications of symbolic data analysis
- Case studies
- Discussion and Q&A
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| Expected Outcome | - Enhanced understanding of Symbolic Data Analysis and its role in the framework of Benzecri’s French school of analyse des données.
- Proficiency in identifying and working with different types of symbolic data.
- Ability to preprocess symbolic data for analysis.
- Mastery of basic and advanced symbolic data analysis techniques.
- Knowledge of clustering, classification, and association rule mining of symbolic data.
- Understanding of practical applications of symbolic data analysis in various contexts.
- Ability to critically analyze and discuss case studies related to symbolic data analysis.
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| Training Methods | - Presentations and lectures, practical examples
- Exchange of views/experiences on national practices
- Exercises
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| Required Reading | None |
| Suggested Reading | - Rosanna Verde, Edwin Diday - Symbolic Data Analysis: A Factorial Approach Based on Fuzzy Coded Data
- Rosanna Verde, Antonio Irpino, Antonio Balzanella – Dimension Reduction Techniques for Distributional Symbolic Data
- Rosanna Verde, Clustering Methods in Symbolic Data Analysis
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| Required Preparation | None |
Trainer(s)/
Lecturer(s) | Rosanna VERDE (Independent expert) Antonio IRPINO (Independent expert) |
