Knowledge Acquisition from Rough Sets Using Merged Decision Rules

Abstract

Rough set theory was proposed by Z. Pawlak in 1982. This theory can mine knowledge based on a decision rule from a database, a web base, a set, and so on. The decision rule is used for data analysis as well as calculating an unknown object. We analyzed time-series data using rough sets. Economic time-series data was predicted using decision rules. However, there are cases where an excessive number of decision rules exist, from which, it is difficult to acquire knowledge. In this paper, we propose a method to reduce the number of decision rules by merging them. Similar to how it is difficult to acquire knowledge from multiple rules, it is also difficult to acquire knowledge from rules with a large number of condition attributes. We propose a method to reduce the number of condition attributes and thereby reduce the number of rules. We analyze time-series data using this proposed method and acquire knowledge for prediction using decision rules. We use TOPIX and the yen–dollar exchange rate as knowledge-acquisition data. We propose a method to facilitate knowledge acquisition by merging rules.