Analysis and Application of Clustering Based on Information Granularity
Jie Chen · 2007
In dealing with complicated problems,the characters of the object can be obtained effectively when the disturbing and nonessential attribute can be wiped off by changing the granular space where the problem located,which make it easier to analyze and solve the problems.In this paper,the analysis of clustering is discussed according to granularity computing.It is assumed that the clustering problems are analyzed under the same granularity(the finest granular space of the problem).The essential of introducing the different comparability functions of clustering is to get a series of equivalence species of different granular space.In practice,problems can be transformed into required granular space,by selecting different comparability functions according to the problem.The transformation form multicolor three-dimension space to monochrome one-dimension can be realized by proposing The License Plate Binary Algorithm based on Information Granularity.Experiments show that the results of this algorithm are more suitable to actual image,have broad generality,and are in favor of recognition following.It is especially predominant in inclined plates or asymmetrical illumination plates.