Subspace Clustering with Gravitation

Data mining is a process of discovering and exploiting hidden patterns from data. Clustering as an important task of data mining divides the observations into groups (clusters), which is according to the principle that the observations in the same cluster are similar, and the ones from different clusters are dissimilar to each other. Subspace clustering enables clustering in subspaces within a data set, which means the clusters could be found not only in the whole space but also in subspaces. The well-known subspace clustering methods have a common problem, the parameters are hard to be decided. To face this issue, a new subspace clustering method based on Bottom-Up method is introduced in this article. It takes a gravitation function to select data and dimensions by using self-comparison technique. The parameter decision is easy, and does not depend on amount of the data, which makes the subspace clustering more practical.