Successful estimation of spectrally active soil texture with Visible and Near-Infrared (VNIR, 400-1200 nm) and Short-Wave-Infrared (SWIR, 1200-2500 nm) spectroscopy depends mostly on the selection of an appropriate data mining algorithm. The aims of this paper were: to compare different data mining algorithms including Partial Least Squares Regression (PLSR), which is the most common technique in soil spectroscopy, Support Vector Machine Regression (SVMR), Boosted Regression Trees (BRT), and Memory Based Learning (MBL) as a very new promising approach for estimating the contents of clay, silt, and sand, to explore whether these methods show differences regarding their ability to predict soil texture from VNIR/SWIR data and to evaluate the interpretability of the results. The dataset consists of 1104 samples from large brown coal mining dumpsites in the Czech Republic. Spectral readings were taken in the laboratory with a fibreoptic ASD FieldSpec III Pro FR spectroradiometer. Leave-one-out cross validation was applied to optimize and validate the models. Comparisons were made in terms of the coefficient of determination (R²_cv) and the Root Mean Square Error of Prediction of Cross Validation (RMSEP_cv). Predictions of the three soil properties by MBL outperformed the accuracy of the other algorithms. It produced the largest R²_cv and smallest RMSEP_cv values, especially for clay, followed by SVMR. Actually, the main goal of this work was to develop a suitable MBL approach for soil spectroscopy, it showed that MBL is a very promising approach to deal with complex soil texture VNIR/SWIR datasets. A systematic comparison like the one presented here is important as the nature of the target function has a strong influence on the performance of the different algorithms.