The mass data in the large scale database has mixed attributes

namely

the mixed data of symbolic data and numerical data

and the quantity of data is complex and difficult to distinguish. Traditional algorithms often ignore the correlation between the two attributes

the calculation is complex

the clustering speed is slow

the effect is poor. A numerical study of mixed database clustering in large-scale data clustering algorithm based on the traditional algorithm

firstly in order to reduce the high complexity

from reasonably extracting large-scale databases of multiple small data sets

all natural clusters contain database on small data sets; similarity matrix similarity is defined to construct small data set based on the then

the similarity data symbols and numerical data calculation; integration

the final result of the sample clustering updated mean and classification

clustering of numeric data mixed in large databases. Simulation results show that the proposed algorithm can get a better clustering result

and is suitable for large scale data clustering processing. The algorithm has a fast calculation speed

a relatively small amount of computation

and a low error rate.