Implementing Clustering for Wellness Application

Abstract

Data analysis has always been useful in business, but nowadays with new data being generated every day it has become a must for companies to stay competitive. Data analysis in wellness can be used to gain useful information about people’s habits, exercises, nutrition, health, and life quality overall. It can be applied on groups of people or individuals not only to learn about them but also to support them with recommendations and service personalization.

This study implements clustering of questionnaire answers from users of Extensive Life Health-e-Living portal which gives insights about the users of the portal and by looking at the results coach should be able to tell in what condition are portal users and what they need to improve in their lifestyle.

The thesis covers data analysis overall and emphasizes on clustering algorithms and tools which are used in the implementation of the service.

Tools used for the development are Ruby on Rails, on which Health-e-Living portal is based, and Ai4R gem which was used to implement clustering. The results are also visualized in the portal using Rickshaw D3.js library.