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Large amounts of data can diminish the efficiency of data mining and may not provide significant inputs to the model. Non-essential attributes add noise to the data, expanding the size of the model. Moreover, model building and scoring leads to consumption of time and system resources, influencing model precision.

Likewise, huge data sets may contain groups of attributes that are associated and may quantify a similar hidden component which can skew the logic of the algorithm. The computation cost associated with algorithmic processing increases with higher dimensionality of processing space, posing challenges for data mining algorithms. Impacts of noise, correlation, and high dimensionality can be minimized by dimension reduction using feature selection and feature extraction [10].