Extraction of spatial association rules based on binary mining algorithm in mobile computing

In mobile computing there are many spatial data correlative with locations, which are very important for mobile intelligent system to extract spatial association among locations that can provide potential and useful information for mobile clients. Hence, aiming to simple transverse association describing spatial association among different spatial objects under the same pattern of association, this paper proposes an approach of extracting spatial association rules based on binary mining algorithm, which firstly uses the method of Circle contained by spatial analysis to extract the values of spatial predicate based on the definitive spatial predicate, specific objective and other given objects around the objective, and then aiming to each objective, transforms them into digital transaction database by binary, finally extracts these spatial association rules from the spatial database with binary mining algorithm introduced by this paper. In mobile computing the mining algorithm uses the method of increasing value to generate candidate frequent item sets and uses binary logical “and” operation to calculate support of item sets in order to reduce running time of mobile intelligent system, which can fast respond to requirement of client. The experiment indicates that efficiency of binary mining algorithm is faster and more efficient than Apriori used in mobile intelligent system.