Social mining-based clustering process for big-data integration
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ORIGINAL RESEARCH
Social mining‑based clustering process for big‑data integration Hoill Jung1 · Kyungyong Chung2 Received: 15 December 2018 / Accepted: 25 April 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract With the development of information technology, ambient intelligence has been combined with various application areas so as to create new convergence service industries. Through IT convergence, human-oriented technologies for improving people’s quality of life has continued to be developed. Healthcare service that has been provided along with the development of various smart IT devices makes it possible to realize more efficient healthcare of people. Therefore, along with such a medical service, the advanced lifecare service for physical and mental health has been demanded. In order to meet the healthcare demands, an advanced healthcare platform has been developed. Lifecare service has been expanded to healthcare, the disease with the highest mortality induced by complications so that the service for disease survivals have been offered. Accordingly, a big-data integration and advanced healthcare platform based on patients’ life logs are developed in order for health service. In this platform, it is possible to establish an optimized model with the knowledge base and predict diseases and complications and judge a degree of risk with the use of information filtering. The conventional filtering based on a data model using scatter life logs makes use of user attribute information only for clustering so that it has low accuracy. Also, in calculating the similarity of actual users, such a method does not apply social relationships. Therefore, this study proposes a social mining based cluster process for big-data integration. The proposed method uses conventional static model information and the information extracted from the social network in order to create reliable user modeling and applies a different level of weight depending on users’ relations. In the clustering process for disease survivals’ health conditions, it is possible to predict their health risk. Based on the risk and expectation of healthcare event occurrence, their health conditions can be improved. Lifecare forecasting model that uses social relation performs social sequence mining using PrefixSpan to complement the weak point that spends a long time to scan it repeatedly in the candidate pattern. For performance evaluation, the social mining based cluster process was compared with a conventional cluster method. More specifically, the estimation accuracy of the conventional model-based cluster method was compared with the accuracy of the social mining based cluster process. As a result, the proposed method in the mining-based healthcare platform had better performance than the conventional model-based cluster method. Keywords Social mining · Ambient intelligence · Big-data integration · Cluster process · Healthcare · Machine learning · Lifecare
1 Introduction * Kyungyong Chung [email protected] Hoill Jung hij
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