The invention provides an urban business-circle cluster partition method based on self-adaptive
DBSCAN density clustering. The method includes the following steps that 1, for all shops of one type ofall cities, the nearest distance is subtracted form 1, the global
DBSCAN clustering
radius epsilon G is obtained, and the
quartile Q of the number distribution of this type of shops of all the citiesis calculated; 2,
longitude and
latitude data of all this type of shops of the city Ci is obtained; 3, whether the number of this type of shops of the city is larger than the Q or not is judged. If the number of this type of shops of the city is larger than the Q, the city shop clustering
radius epsilon is independently calculated, MinPts=1, and
DBSCAN density clustering is conducted; otherwise, the global clustering
radius epsilon G is used, MinPts=1, and DBSCAN density clustering is conducted. For cities different in shop number and scale, different business-circle cluster partition strategies are performed, the robustness of the geographical location cluster partition result is improved, the business-circle
layout characteristics of one type of shops in different cities are effectivelyreflected, and a subsequent recommendation
system thus can easily explore the user behavior about the change of interest points in geographical positions.