Analysing citizen profiles with data mining

Abstract

There is an exponential growth in issues attached with lifestyles of Sri Lankans over the past few decades. These may contribute to low down the life quality within citizens. In Sri Lanka, there are no adequate researches in the field of analyzing lifestyle data. Though there are few researches which have analyzed the causes for the socio-economic problems, such approaches are not capable of handling big data effectively and not efficient in predicting or describing the issues attach with lifestyle. Hence, the research has been conducted to analyze citizen profiles in effective way to explore different lifestyle issues. It is hypothesized that analyzing citizen profiles can be done through data mining according to the output want to achieve through predictive or descriptive techniques. The solution takes HIES data set as the input and predict the factors attach with a particular lifestyle issue or describe specific lifestyle issue with its associative causes. Having received the input, this approach preprocessed the dataset to remove the anomalies. Then build data models to represent the lifestyle issue by extracting attributes from HIES data set. Then proceed with pattern recognition for the issues. The important patterns recognized through this approach will be useful for government and policy makers to set up appropriate government policies to uplift the life quality of citizen. The overall design of the research consists oftwo research question, one question used predictive mining based solution and other one is based on descriptive mining. Classification in data mining used in finding the factors and their relationships that associated with no schooling and dropouts as those were predictive mining tasks. Clustering is used to explore the relationship between chronic diseases and family. was The overall research is designed using WEKA data mining tool and SPSS statistical tool. Finally, the data models build for citizen profile analysis using data mining techniques are evaluated for their performance using measurements such as value for accuracy, error rate, training time, TP rate, FP rate and ROC measurement.