The application of a top-down algorithm in neighboring class set mining

This paper focuses on character of present frequent neighboring class set mining algorithms which is suitable for mining short frequent neighboring class set, and introduces a top-down algorithm in frequent neighboring class set mining. This algorithm is suitable for mining long frequent neighboring class set in large spatial data according to top-down strategy, and it creates digital database of neighboring class set via neighboring class bit sequence. The algorithm generates candidate frequent neighboring class set via top-down search strategy, namely, it gains k-neighboring class set as candidate frequent items by computing k-subset of (k+1)-non frequent neighboring class set. The mining algorithm computes support of candidate frequent neighboring class set by digit logical operation. The algorithm improves mining efficiency through these two methods. The result of experiment indicates that the algorithm is faster and more efficient than present algorithms when mining long frequent neighboring class set in large spatial data.