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Due to visiting data once during the processing data in stream, the performance of processing data is crucial.
In this method, data which are active during window time W i, will be expired during window time W i+1. Consequently, visiting data in whole dataset is possibly once and during the window time only.
This main property of data stream will be associated with some difficulties in clustering data stream (due to clustering is the focus task in this paper) which include: 1. Due to visiting data once during the processing data in stream, the performance of processing data is crucial.
It is possible to enumerate two main problems in data stream clustering which are concept change and visiting data once and there are different groups of solutions for data stream clustering from different perspectives that we summarized them in Table 1.
Visiting data included dates, medical care facilities, genders, dates of birth, and the three major diagnoses for visits coded in International Classification of Disease, Ninth Revision, Clinical Modification (ICD-9-CM) format.
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Follow-up data could be automatically linked to pre-visit data using email addresses, while other possible identifying information (income, age, house type) would be kept separate.4.4
During the follow up visit, data collectors recorded the location of each of the installed alarms and tested them by pushing the test button.
To approximate this, we used emergency department (ED) visit data from our syndromic surveillance system.
First and second visit data from the TRI database and 8 and 12 weeks visit data from the RESET study were used to calculate ICC.
Twenty six facilities reported ART client data; and 22 reported both ART and OPD visit data.
Similar results were obtained for the follow up visit (data not shown).
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