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

Abstract

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.

An Algorithm of Frequent Neighboring Class Set Mining with Constraint Class

“…TDA written in [5] adopts top-down strategy to generate candidate frequent neighboring class set, which is made of three stages, firstly, computing the 1st m-candidate frequent neighboring class set which contains all classes, and then generating (m-1)-candidate frequent neighboring class set, let (m-1) be k, and generating all (k-1)-frequent neighboring class set (k>3) by iteration. But it isn't also able to extract frequent neighboring class set with constraint class.…”

“…At present, in spatial data mining, there are mainly three kinds methods of mining spatial association rules written in [3], such as, layer covered based on clustering written in [3], mining method based on spatial transaction written in [2, 4, 5 and 6] and mining method based on non-spatial transaction written in [3]. We use the first two methods to extract frequent neighboring class set written in [4, 5 and 6], but AMFNCS written in [4] and TDA written in [5] are not able to efficient extract frequent neighboring class set with constraint class, and MFNCSWCC written in [6] via iterative search is unsuitable for extracting long frequent neighboring class set with constraint class. Hence, this paper proposes an algorithm of frequent neighboring class set mining with constraint class based on breadth first search, denoted by FNCSMBFS, which is suitable for mining long frequent neighboring class set with constraint class in large spatial database.…”