Efficient Rules Extraction using Rough Set Theory for Weather Data Analysis

Abstract

The system extracts optimal rule from weather data set based on rough set theory. The main idea of rough set theory is to obtain as simple as rules from the given database by reducing the database while holding the original degree of consistency. In order to find the optimal rule of weather from the historical data it provide easy and accurate for the weather forecast. This system included the processes of indiscernibility, set approximation, attributes reduction, rules extraction and optimal rule selection. GDT-RS (Generalization Distribution Table for Rough Sets) are used for the rules extraction and optimal rule selection. This system analyzes the relationship between the weather condition attribute and other attributes of weather data set by calculating the dependency and accuracy between them.