ChiMerge Algorithm: A Data Discretization Method

In this article, we provide a comprehensive overview of the ChiMerge algorithm, a fundamental data discretization technique. The primary objective of this algorithm is to transform continuous data into discrete data, making it more suitable for data mining and machine learning applications. The core principle of ChiMerge involves iteratively merging adjacent intervals into new intervals until predefined stopping criteria are met. These criteria typically include either a maximum number of intervals or a minimum interval width. Implementation-wise, the algorithm typically requires sorting continuous values, initializing intervals, computing chi-square statistics between adjacent intervals, and merging pairs with the lowest chi-square values. The algorithm's efficiency stems from its use of statistical significance testing to determine optimal merging points. ChiMerge stands as a relatively simple yet highly effective algorithm that finds extensive application in data analysis workflows. Key implementation considerations include handling boundary conditions, optimizing chi-square calculations, and determining appropriate significance thresholds for merging decisions.