A new hybridization of DBSCAN and fuzzy earthworm optimization algorithm for data cube clustering

Data aggregation from different databases into a data warehouse creates multidimensional data such as data cubes. With regard to the 3D structure of data, data cube clustering has significant challenges to perform on data cube. In this paper, new preprocessing techniques and a novel hybridization of DBSCAN and fuzzy earthworm optimization algorithm (EWOA) are proposed to solve the challenges. Proposed preprocessing consists of an assigned address to each cube cell and dimension move to create a related 2D data from the data cube and new similarity metric. The DBSCAN algorithm, as a density-based clustering algorithm, is adopted based on both Euclidean and newly proposed similarity metric, which are called DBSCAN1 and DBSCAN2 for the related 2D data. A new hybridization of the EWOA and DBSCAN is proposed to improve the DBSCAN, and it is called EWOA-DBSCAN. Also, to dynamically tune parameters of EWOA, a fuzzy logic controller is designed with two fuzzy group rules of Mamdani (EWOA-DBSCAN-Mamdani) and Sugeno (EWOA-DBSCAN-Sugeno), separately. These ideas are proposed to present efficient and flexible unsupervised analysis for a data cube by utilizing a meta-heuristic algorithm to optimize DBSCAN's parameters and increasing the efficiency of the idea by applying dynamic tuning parameters of the algorithm. To evaluate the efficiency, the proposed algorithms are compared with DBSCAN1 and GA-DBSCAN1, GA-DBSCAN1-Mamdani and GA-DBSCAN1-Sugeno. The experimental results, consisting of 20 runs, indicate that the proposed ideas achieved their targets.