運用連續性資料之間斷化技術的主要目的是為了提高資料探勘技術的效能，包括精確度、效率、精簡度、以及實際效果。本研究專題係以三種不同的模糊隸屬函數來轉換連續性資料並求得其隸屬強度值，最後再比較其探勘結果精簡度的優劣。此三種模糊隸屬函數分別為採用有四分之一重疊的三角形函數、降低粒子化程度(Decreasing information granularity, DIG)、與提高粒子化程度(Increasing information granularity, IIG)，其中DIG將採正弦函數(Sine function)為轉換函數，而IIG 則將以三角形為中心，對稱於DIG 所構成的函數為基礎。本研究搜集了十八個實務性資料庫做為測試之用，探勘法係採ID3。比較結果發現：DIG 有比較好的效能。
Knowledge discovery has been successfully used in acquiring domain knowledge from large databases. It is a required process to transform continuous data into linguistic ones. The transformation function used in the previous research was triangle membership function. However, the impact of other type of information granularity remains unknown. It is believed that different level of information granularity will result in different knowledge discovery performance. In this research, two tasks were conducted. The first one contained four rocesses. They were collecting real-life datasets, granulizing continuous datasets via Increasing Information Granularity (IIG) and Decreasing Information Granularity (DIG), discovering decision rules via ID3, and documenting the mined results. The DIG was based on sine function while IIG the function that is centered on the triangle function and symmetrical to the DIG. The second task focused on the empirical evaluation where eighteen real-life datasets were utilized. The result indicated that DIG showed a better performance.