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PurposeIn response to the need for more advanced and longitudinal data concerning chronic diseases, behavioural risk factors and social support systems in India, the SWADES (Social Well-being and Determinants of Health Study) was established.ParticipantsAt baseline, 997 adults aged 30 years and over, living in the semi-urban area were interviewed in their home.Findings to dateData collected included self-reports of demographic details, health, depression, morbid conditions and healthcare utilisation, risk factors (physical, behavioural and social) of chronic diseases, common mental disorders, out-of-pocket expenditure, social support network, social cohesion, disability, education and wealth. Objective data for hypertension, diabetes and cognitive function were also collected.Future plansThe first annual follow-up interviews were completed in 2019; the subsequent annual follow-up will be conducted until 2030. The SWADES data are held at the International Centre for Consortium Research in Social Care (ICRS), Rajagiri College of Social Science, Kerala, India. Procedures for data access, information on collaborations, publications and other details can be found at (http://icrs.in).

Abstract-Over the years the academic records of thousands of students have accumulated in educational institutions and most of these data are available in digital format. Mining these huge volumes of data may gain a deeper insight and can throw some light on planning pedagogical approaches and strategies in the future. We propose to formulate this problem as a data mining task and use k-means clustering and fuzzy c-means clustering algorithms to evolve hidden patterns.

Detecting crime from data analysis can be difficult because daily activities of criminal generate large amounts of data. The police records exist in various formats and the quality of analysis greatly depends on the background knowledge of the analyst. This paper proposes a simple correlation clustering algorithm which aims at finding illegal activities of professional identity fraudsters based on knowledge discovered from their own histories.