Web usage mining for analysing elder self-care behavior patterns

Hung, Yu-Shiang; Chen, Kuei-Ling B.; Yang, Chi‐Ta; Deng, Guang-Feng

doi:10.1016/j.eswa.2012.08.037

Cited by 36 publications

(14 citation statements)

References 23 publications

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“…The use of data mining in psychotherapy is still scarce, although some studies have found interesting usages for the health promotion. As an example, in a study conducted with data collected from elderly people under self-care programs which was analyzed with web usage mining, results showed association relationships, interest and sequence-based representation schemes that could be used for research in medicine, public health, nursing and psychology and for policy-making in the health care domain [19].…”

Section: Computational Techniques For the Analysis Of Web-basedmentioning

confidence: 99%

A Revision of Preventive Web-based Psychotherapies in Subjects at Risk of Mental Disorders

Sánchez-Gutiérrez¹,

Barbeito²,

Calvo³

2017

IJIMAI

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Section: Computational Techniques For the Analysis Of Web-basedmentioning

confidence: 99%

A Revision of Preventive Web-based Psychotherapies in Subjects at Risk of Mental Disorders

Sánchez-Gutiérrez¹,

Barbeito²,

Calvo³

2017

IJIMAI

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“…Hung et al (2013) suggested a paper about web usage mining for scrutinzing elder self care behavior patterns. The main intent of this study was to understand the self care service.…”

Section: Related Workmentioning

confidence: 99%

Recommendation Engine Formation Using Depth First Search and Genetic Approach

Kanchana¹,

Sujatha²

2015

Journal of Computer Science

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Abstract:The requirement of online users in the website varies dynamically. The recommendation of web pages consisting of user expected information and data is performed by the online recommendation system. The recommendation engine must be selfadaptive and accurate. The existing algorithm uses Depth First Search (DFS) and bee's foraging approach to create navigation profiles by categorizing the current user activity. The prediction of navigations that are most expected to be visited by online users is also performed. In this study, the recommendation engine formation with optimized resource such as memory, CPU usage and minimum time consumption is proposed using DFS and Genetic Approach (GA). Here, initially the cluster formation is achieved using DFS approach. The method creates an eminent browsing pattern for each user using live session window. The performance of the approach is compared with the existing forager agent. The experimental results show that the proposed approach outperforms the existing methods in accomplishing accurate classification and anticipation of future navigation for the current online user.

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“…Parameters were page categories as state that each Markov chain represents the behaviour of a specific subgroup and categorize page with K-means & Kmeans + Fuzy ART methods, training algorithm was EM on Information Server (IIS) logs for msnbc.com data set for the entire day of September, 28, 1999. Yu-Shiang Hung et al, [16] proposed combined Markov model with ART2-enhance in web usage mining, each Markov chain represents the behaviour of a specific subgroup and training algorithm was EM on all of march, 2012, 3391 sessions of 157 elders data set were identified for analysis. Yi Xie et al, [17] proposed a Large-scale hidden semi Markov model for web security in web usage mining.…”

Section: Review Of Research On the Application Of Markov Models In Wementioning

confidence: 99%