• algorithms
• algoritmi
• analisi di eventi culturali
• analysis of cultural events
• Bisecting K-Means
• business intelligence analysis
• clustering
• data
• data driven
• data mining
• DBSCAN
• Internet Festival of Pisa
• Optics
• pattern mining
• powerBI
• PrefixSpan
• python
• spatial clustering

The aim of this thesis is to draw up a prototype on a methodology that can be used to analyze the data of cultural events. It is important to enhance cultural organizations’ awareness about the great power of the data they already have or that they can produce, in order to fully comprehend and build up different aspects of their world: most organizations make annual reports, project reports, collect financial data, carry out social media and media coverage analyses, and this gives them a great source of knowledge. Moreover, regular reviews of the data collected can produce useful insights for the future events or activities to be carried out. This part of the analysis is mainly carried out with Business Intelligence tools, which are easier to use and to understand by the majority of people. But this kind of study can receive a great boost with the exploitation of Data Science technologies.
This paper will focus on comparing tools from a Business Intelligence and a Data Mining analysis, in order to enhance the cultural and creative industries. Hence, the study is partially included in the Me-Mind project, which is co-financed by the European Commission's “Creative Europe” program, having one major objective, that is to provide cultural and creative industries with different data-driven decision making business models, for communicating and making the impacts more understandable to visitors, to the stakeholders and policy makers. Here, we will conduct the analysis on the Internet Festival of Pisa.