A Clustering Algorithm Based on Discretized Interval Value

In order to improve the quality of traditional clustering algorithm and prevent the distribution of data from affecting the clustering algorithm greatly, a clustering algorithm based on interval value was proposed. Depending on the consistency of condition attributes and decision attributes in the decision table, the data was discretized and attributes were reduced by using data super-cube and information entropy. Based on the above, the algorithm can use the additivity of set feature vector to cluster data just by scanning the decision table only one time. Experimental results indicate that the algorithm is efficient and effective