Gravitational Ensemble Clustering

Data mining is one of the helpful and effective data analysis techniques that enable the extraction of interesting structures and knowledge from a large amount of data.Clustering is an important data mining task that refers to the process of categorizing data objects into cohesive groups calledclusters. There are many clustering approaches proposed inthe literature with different quality/complexity tradeoffs. It is well known that no clustering method can sufficiently handleall types of cluster structures and properties (e.g. shape, size, overlapping, and density). The idea of combining differentclustering results (cluster ensemble or clustering aggregation) emerged as an approach to overcome the weakness of singlealgorithms and further improve their performances. In thispaper, a novel consensus function based on the theory of gravity is presented which is called Gravitational EnsembleClustering (GEC) . The proposed method combines weak clustering algorithms such as the K-means algorithm usinggravitational clustering concepts. The proposed method is capable of the identification of true underlying clusters with arbitrary shapes, sizes and densities. Computationalexperiments were conducted to test the performance of the GEC approach using artificial and benchmark datasets.Undertaken experimental results illustrate the versatility androbustness of the proposed method, as compared to individual clusterings produced by well known clustering algorithms, and compared to other ensemble combination methods.

Keywords:

Ensemble clustering , Gravitational theory , Gravitational clustering