Interval Similarity-Based Quantization Method for Continuous Data

Data quantization methods for continuous attributes play an extremely important role in artificial intelligence, data mining and machine learning because discrete values of attributes are required in most classification methods. In this paper, we present an interval similarity-based quantization method for continuous data. It defines an interval similarity criterion which is regarded as a new merging standard in the process of quantization. In addition, a heuristic quantization algorithm is proposed to achieve a satisfying quantization result with the aim to improve the performance of inductive learning algorithms. The new algorithm realizes fair standard and quantifying the real value attributes exactly and reasonably. Empirical experiments on UCI real data sets show that our proposed algorithm generates a better quantization scheme that improves the classification accuracy of inductive learning than existing quantization algorithms.