A rough set based subspace clustering technique for high dimensional data

Clustering for High Dimensional Data: Density based Subspace Clustering Algorithms

Finding clusters in high dimensional data is a challenging task as the high dimensional data comprises hundreds of attributes. Subspace clustering is an evolving methodology which, instead of finding clusters in the entire feature space, it aims at finding clusters in various overlapping or non-overlapping subspaces of the high dimensional dataset. Density based subspace clustering algorithms t...

An Efficient Technique for Clustering High Dimensional Data Set

In the modern world, advance technologies produce huge amount of data with many objects and dimensions. Traditional clustering algorithms do not perform well in the high dimensional data sets as similarity measures are no more meaningful, hence the data objects are equidistant from each other in high dimensions. Some traditional algorithms produce local optimum results as they start with random...

Subspace Clustering for High Dimensional Categorical Data

A fundamental operation in data mining is to partition a given dataset into clusters such that objects in the same cluster are more similar to each other than objects in different clusters according to some defined criteria [2]. These criteria are usually defined in the form of some distance, and similarity is hence defined as follows, the smaller the distance is, the more similar the objects a...

Soft Subspace Clustering for High-Dimensional Data

High dimensional data is a phenomenon in real-world data mining applications. Text data is a typical example. In text mining, a text document is viewed as a vector of terms whose dimension is equal to the total number of unique terms in a data set, which is usually in thousands. High dimensional data occurs in business as well. In retails, for example, to effectively manage supplier relationshi...

Constraint Based Subspace Clustering for High Dimensional Uncertain Data