Considering from the law of gravity

the influence between particles includes distance and direction.After discussing the vector influence between data objects

it is applied in clustering algorithm.Vector influence function is presented from the scalar influence function and direction influence function.Two methods—similarity and sum are introduced to compute the direction influence.The algorithm deals with the core point by getting the projection of the points in its neighborhood to judge whether it is uniformity influence.Only uniformity influence points can be expanded to form clusters.The theoretical analysis and experimental results indicate that the algorithm can discover clusters with arbitrary shape and can effectively eliminate noise such as boundary sparse points.It solves the difficulties of clustering high dimensional spatial data such as the spatial distribution of the data

not obvious boundary between clusters

too many noise data points and the phenomenon that the distance between the nearest and farthest neighbors of a data point goes to zero etc.The algorithm improves the accuracy of clustering and offers better results of clustering on various data sets.It executes effectively and efficiently.The algorithm is scalable and general.While transforming the semi-structure data into Euclid space

it will always appear boundary sparse objects

VICA can deal with the noise effectively.Therefore

the algorithm is proper with the high dimension data set

and also can be applied in the semi-structure data clustering.