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Ordinal decision problems are very common in real-life. As a result, ordinal classification models have drawn much attention in recent years. Many ordinal problem domains assume that the output is monotonously related to the input, and some ordinal data mining models ensure this property while classifying. However, no one has ever reported how accurate these models are in presence of varying levels of non-monotone noise. In order to do that researchers need an easy-to-use tool for generating artificial ordinal datasets which contain both an arbitrary monotone pattern as well as user-specified levels of non-monotone noise. An algorithm that generates such datasets is presented here in detail for the first time. Two versions of the algorithm are discussed. The first is more time consuming. It generates purely monotone datasets as the base of the computation. Later, non-monotone noise is incrementally inserted to the dataset. The second version is basically similar, but it is significantly faster. It begins with the generation of almost monotone datasets before introducing the noise. Theoretical and empirical studies of the two versions are provided, showing that the second, faster, algorithm is sufficient for almost all practical applications. Some useful information about the two algorithms and suggestions for further research are also discussed.

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