automatic parameter determination in subspace clustering with gravitation function
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 with determination of parameters. To face this issue, a new subspace clustering method based on bottom-up method is introduced in this article. In contrast to other methods, this approach applies a gravitation function to select data and dimensions by using a self-comparison technique. The new method can determine parameters simply and independently of amount of the data, which makes the subspace clustering more practical.
subspace clustering gravitation high dimensional data