Abstract
Spatial Co-location pattern mining is an important research content of data mining. Traditional spatial co-location pattern mining and those based on fuzzy sets can only deal with certain data and uncertain data expressed by a fuzzy membership degree, which the data that there are many possible values of fuzzy membership degree cannot be processed. To this, combined with the theory and method of hesitant fuzzy sets, a method of spatial co-location pattern mining based on hesitant fuzzy location is proposed, which the hesitant fuzzy location of spatial feature instances is defined by using the characteristic that hesitant fuzzy sets can express multiple possible fuzzy membership degrees at the same time. Hesitation fuzzy score space proximity is defined by score function of hesitant fuzzy sets, and then the participation ratio and participation index of hesitant fuzzy location are defined by combined with the hesitant fuzzy location and hesitant fuzzy score function. The algorithm flow of the proposed method is given. The experimental results show that the proposed method is effective and it can find more frequent spatial co-location patterns compared with the existing method.
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