A fast clustering approach for large multidimensional data
Hajar Rehioui, Abdellah Idrissi · International Journal of Business Intelligence and Data Mining · 2017
Density-based clustering is a strong family of clustering methods. The strength of this family is its ability to classify data of arbitrary shapes and to omit the noise. Among them density-based clustering (DENCLUE), which is one of the well-known powerful density-based clustering methods. DENCLUE is based on the concept of the hill climbing algorithm. In order to find the clusters, DENCLUE has to reach a set of points called density attractors. Despite the advantages of DENCLUE, it remains sensitive to the growth of the size of data and of the dimensionality, in the fact that the density attractors are calculated of each point in the input data. In this paper, in the aim to overcome the DENCLUE shortcoming, we propose an efficient approach. This approach replaces the concept of the density attractor by a new concept which is 'the hyper-cube representative'. The experimental results, provided from several datasets, prove that our approach finds a trade-off between the performance of clustering and the fast response time. In this way, the proposed clustering methods work efficiently for large of multidimensional data.