Erna Piantari, Eki Nugraha
Spatio-temporal data has enormous potential to be a source of information and knowledge. One of the research topics in spatio-temporal data analysis is the search for frequent trajectory patterns that aim to analyze the movement patterns of objects. However, trajectory mining is a challenging activity due to noise and the limitations of data collection technology. The collected trajectory data generally has a spatial or temporal bias problem, one of which is the existence of a period of “silent duration” or the unavailability of data for a moving object in a certain period of time. This can happen because the data is lost, or because of a particular sampling strategy. The silent duration phenomenon that occurs in spatio-temporal data often follows data damage therefore the data mining process that was carried out did not produce true knowledge or patterns. In this study, the Linear Interpolation method will be used to deal with data damage caused by the missing value during silent duration. Linear Interpolation is a method that is able to search or produce hidden values. This process was carried out in the preprocessing phase to get complete and clean data. © 2020, Springer Nature Switzerland AG.
Universitas Pendidikan Indonesia, Setiabudi Street 229, Bandung, Indonesia
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