Scientists in many researches have been using computer technologies lately. GIS, GPS have been helping scientists in doing many kinds of researches. Geographical data as a result from GPS were available in electronic format. This type of data can be treated as a spatial data. And by using colocation pattern mining, we would discover associations between spatial features. The first thing we do was developed a data set generator. Data sets that are generated by data set generator then processed using the proposed approach. The system we proposed was a two steps system. The first step was doing a segmentation to produce the transaction from the data set. The segmentation was using a fix threshold segmentation and the threshold was 3*sigma. Sigma in this study is our way to measure closeness of a point to its neighbors. Sigma is a distance value that will bring the entropy of the whole data set into it minimum, sigma was calculated using data field model. And the second one was doing a fuzzy association rule mining where we introduce the transaction into a fuzzy membership function. After fuzzfied the data set then we counted the fuzzy support values and fuzzy confidence values. The infrequent rules then pruned using an apriori-like algorithm. The result of the approach then come as these manners, feature a will be whether near or close or far from feature b.
