Data Clustering Using Honey Bee Mating Optimization Cell Algorithm

Nowadays, with regard to huge data volumes and increasing rate of data generation,the need for data analysis methods like data mining is increased, and one of the most importantbranches of data mining is clustering. Due to with the real time requirement and size of data thatshould be processed, these methods must be able to act very fast. One of these methods use theCellular Automata that power of parallelism is very high. furthermore, regarding the growth ofheuristic evolutionary methods in recent decades, these techniques as a tool to search andoptimization in different fields of science,commerce and engineering and is also easier to use andthe ability to achieve close to optimal response including the absolute reasons for the success ofthese methods. In this paper, a combination model with name HBMCA for clustering data wehave presented, in which, each cell of the cellular automata we have considered, HBM algorithmrun an acceptable number of mating flights , and then is compared with other cells and thisresults so much work continues to be of optimum solution. The developed algorithm, comparedwith K-Means, and results indicate that the algorithm provided better results against othermethods and also shows the ability and the capability of this method for clustering data.

Keywords:

Data mining , Clustering , cellular automata , Honey Bee Mating Optimization Algorithm