Main Article Content
Abstract
The partitional and incremental clustering are the common models in mining data in large databases.
However, some models are better than the others due to the types of data, time complexity, and space
requirement. This paper describes the performance of partitional and incremental models based on the number
of clusters and threshold values. Experimental studies shows that partitional clustering outperformed when the
number of cluster increased, while the incremental clustering outperformed when the threshold value decreased.
Keywords: Clustering, partitional, incremental, distance.
However, some models are better than the others due to the types of data, time complexity, and space
requirement. This paper describes the performance of partitional and incremental models based on the number
of clusters and threshold values. Experimental studies shows that partitional clustering outperformed when the
number of cluster increased, while the incremental clustering outperformed when the threshold value decreased.
Keywords: Clustering, partitional, incremental, distance.