Abstract

In this study, data mining of slim life mode based on cycle behavior is propsed. The mining of the periodic behavior is divided into four stages. The first two stages is data pre-processing stage: Firstly, parsing stay point sequence from data sequence of the original location history. Here stay point represent the geographic area to a person’s stay for some time; Secondly, cluster mining the sequence of stay point, find out the significant places, such as company, supermarket, home location, etc. Thirdly, mining periodic on the significant places. Take a place as a reference point; abstract the original location history data into binary sequence by the location point in or out the place. Then, combination two popular signal processing method fast Fourier and autocorrelation find the periods of every place. Fourthly, mining the periodic behavior of the places with the same periods, in this article, first construct the periodic behavior probabilistic model, then use the method based on the hierarchical clustering to mining the periodic behavior between different places. At last, an example is introduced.

Keywords:

Cycle behavior, data mining, slim life mode,

References

Competing interests

The authors have no competing interests.

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Copyright

The authors have no competing interests.