The rapid growth of the elderly population has increased the need to support elders in maintaining independent
and healthy lifestyles in their homes rather than through more expensive and isolated care facilities.
Self-care can improve the competence of elderly participants in managing their own health
conditions without leaving home. This main purpose of this study is to understand the self-care behavior
of elderly participants in a developed self-care service system that provides self-care service and to analyze
the daily self-care activities and health status of elders who live at home alone.
To understand elder self-care patterns, log data from actual cases of elder self-care service were collected
and analysed by Web usage mining. This study analysed 3391 sessions of 157 elders for the month
of March, 2012. First, self-care use cycle, time, function numbers, and the depth and extent (range) of services
were statistically analysed. Association rules were then used for data mining to find relationship
between these functions of self-care behavior. Second, data from interest-based representation schemes
were used to construct elder sessions. The ART2-enhance K-mean algorithm was then used to mine cluster
patterns. Finally, sequential profiles for elder self-care behavior patterns were captured by applying
sequence-based representation schemes in association with Markov models and ART2-enhanced K-mean
clustering algorithms for sequence behavior mining cluster patterns for the elders. The analysis results
can be used for research in medicine, public health, nursing and psychology and for policy-making in
the health care domain.