Can an algorithm tested only on artificial data be accepted in a high rank conference? I devised a classification algorithm which is useful for specific complicated distribution of classes of data. The method works good on the artificial data which cannot be solved by typical algorithms in the literature. But yet i couldn't find any real data to evaluate the algorithm on real problems.
Also there is mathematical support for the basis of the approach and the way it works. 
So i was wondering if an algorithm with good mathematical support but no evaluation on real data has any chance to get accepted in computer science conferences like SDM or CVPR?
But yet i couldn't find any real data to evaluate the algorithm on real problems. 


There exist a good site on benchmark datasets in machine learning,  uci machine learning repository that you can use these datasets as well as the artificial tested data. 


  So i was wondering if an algorithm with good mathematical support but no evaluation on real data has any chance to get accepted in computer science conferences like SDM or CVPR?


The answer is yes if your approach is backed by theoretical support. On validation of attained results, you should provide intuition about how your proposed approach works on a class of problems via the theoretical results if you have such ones'.