Improvement assessment method for special kids by observing the social and behavioural activities using data mining techniques

Abstract

Data mining identifies heterogeneous data that has diverse information. Clustering is the process of gathering unique items into classes, were most clustering methods are based on numerical or categorical attributes which is used in software. In this proposed work, k-mean clustering under unsupervised learning algorithm is used for prediction. Using clinical data of special kids, clustering is made and categorised using rank with relevant symptoms. In this context, the data makes an earlier statistical impact on categorisation and easy detection of associated conditions of a child. The proposed method has validated the database of special kids' information with global purity. It also mines the expressional pattern and values that is reported. The trainers can use this report to train the special kids in the rehabilitation to assess the improvement in special kids by recording and analysing the individual activities of special kids over a period of time to reach vocational training.