Constrained association rule mining algorithm for travel data

With rapid development of the tourism industry,an effective approach emerges to analyze tourism market and predict the influence on the relative industries,which builds upon mining various types of travelers and inherent,hidden relativity among different environmental factors from the gigantic quantity of industrial data.This paper proposes a new association rule algorithm by combining the unique characters of tourism data based on available algorithms.The algorithm is a parallel data-mining algorithm,which is constrained by the available association rule.Meanwhile,it is also restricted by the new association rule mentioned above,called the association-extended route constraint,which can solve problems the old association rule can not.The algorithm which makes the proper use of the"MapReduce"parallel mechanism,can produce item sets under the association-extended route rule,and increase the after-parallel efficiency.At the same time,it can optimize the iterative search of th"eApriori"algorithm,bringing in th"esecond"efficiency improvement.So we can control the whole tourism industry, and adapt the macro industrial strategies more appropriate.