An effort to select a preferable metaheuristic model for knowledge discovery in data mining

Abstract

Swarm intelligence computation is becoming an important topic in the field of decision making, and is successfully applied in many fields, which is indicating a fairly great potential for development. Recently, much research work has been carried out by swarm intelligence in different fields viz. sensor network, robotic management, microwave filter design, data mining applications etc. In this paper, an effort has been made to make a comparison on the performance of fuzzy logic with different membership function, artificial neural network and swarm intelligence models. The models with minimum errors have been given preference for selection towards the decision making of information. The same methods can also be cross-checked by residual analysis and a new unknown data set to verify the earlier proposed observation. Initially the statistical techniques have been applied on the data set to select the preferable models but it has been observed that statistical technique errors are large. Therefore, under the fuzzy logic, different membership functions have been used to select the preferable membership function for fuzzy logic for the available data. In this paper further effort has also been made to change the expression of the computing fitness function of the path in artificial bee colony to achieve the best result.