Implementation Of LEM2 algorithm On FPGA

Abstract

Rough Set Theory (RST), proposed by Z Pawlak, is a new mathematical model for uncertain data [2]. Tools based on RST are mainly useful for data mining tasks such as classification and rule mining. Rule Induction is part of Machine learning in which the rules are extracted from set of observations [10]. Rules generation always have important role in data mining and provide some connection between attributes which are helpful for decision making. A problem for conventional rule algorithms is that there are too many rules generated which are very difficult to analyze [1]. This paper proposes a rough set based approach to generate rules from an consistent information system. The preprocessed data collected from LA (Lower Approximation) and UA (Upper Approximation) concepts [12]. The paper includes implementation of LEM2 algorithm for different count of conditional attributes considering sixteen cases or objects. By increasing conditional attributes in our design the circuit is affected by increasing LUT utilization, increasing register count, increasing Power, decreasing speed, increasing Area and increasing gate count also.