Analysing the Needs of Homeless People Using Feature Selection and Mining Association Rules
This work addresses the challenge for non-governmental organizations in Europe to efficiently collect and analyze data on homeless people, though it appears incremental in applying existing AI methods to a specific domain.
The paper tackled the problem of analyzing data on homeless people by developing a mobile app for data collection and an AI software using feature selection and association rules mining, which identified relevant features and interesting association rules from collected data.
Homelessness is a social and health problem with great repercussions in Europe. Many non-governmental organisations help homeless people by collecting and analysing large amounts of information about them. However, these tasks are not always easy to perform, and hinder other of the organisations duties. The SINTECH project was created to tackle this issue proposing two different tools: a mobile application to quickly and easily collect data; and a software based on artificial intelligence which obtains interesting information from the collected data. The first one has been distributed to some Spanish organisations which are using it to conduct surveys of homeless people. The second tool implements different feature selection and association rules mining methods. These artificial intelligence techniques have allowed us to identify the most relevant features and some interesting association rules from previously collected homeless data.