Multivariate data modelling aims to predict unknown function values through an established mathematical model. It is essential to construct an analytical structure using the given set of high dimensional data points with corresponding function values. The level of multivariance directly affects the modelling process. Increase in the number of independent variables makes the standard numerical methods incapable of obtaining the sought analytical structure. This work aims to overcome the difficulties of high multivariance and to improve the modelling quality by carrying out two main steps: data clustering and data partitioning. Data clustering step deals with dividing the whole problem domain into several clusters by performing k-means clustering algorithm. Data partitioning step performs the Enhanced Multivariance Product Representation method to partition the high dimensional data set of each cluster. The analytical structure is obtained through the partitioned data for each cluster and can be used to predict the unknown function values. (C) 2017 Elsevier B.V. All rights reserved.