This book discusses the potential meta-ensemble algorithms by explaining and testing technique based on a combination of ensemble and PCA statistics that can determine the presence of outliers in high dimensional data sets and to minimize the effects of outliers in the final result. The main contribution concerns fusion ensemble algorithm for topology-preserving map, called the Weighted Voting Superposition (WeVoS), which has been designed to enhance data exploration with visualization of 2-D over multi-dimensional data sets. Generic algorithm is applied in combination with some other models taken from the family of topology preserving map, such as SOM, ViSOM, Max-SIM and SIM. All algorithms are tested on various sets of artificial data and in several machine-learning data are most commonly set to strengthen their theoretical nature.