Mining top-k approximate closed patterns in an imprecise database
by Xiaomei Yu; Hong Wang; Xiangwei Zheng
International Journal of Grid and Utility Computing (IJGUC), Vol. 9, No. 2, 2018

Abstract: Over the last few years, the growth of data is exponential, leading to colossal amounts of information being produced by computational systems. Meanwhile, the data in real-life applications are usually incomplete and imprecise, which poses big challenges for researchers to obtain exact and valid analytical results with traditional frequent pattern mining methods. Since the potential faults can break the original characteristics of data patterns into multiple small fragments, it is impossible to recover the long true patterns from these fragments. To explore the huge amount of imprecise data by means of frequent pattern mining, we propose a service-oriented model that enables a new way of service provisioning based on users' QoS (quality of service) requirements. The novel model is developed to solve the problem of mining top-k approximate closed patterns in imprecise databases and will be further applied to diagnosis and treatment of potential patients in online medical applications. We test the novel model in an imprecise medical database and the experimental results show that the new model can successfully improve the health services for online customers.

Online publication date: Mon, 14-May-2018

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Grid and Utility Computing (IJGUC):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com