… cluster purity with ROGUE before and after clustering or re-clustering, we were able to detect
low-purity clusters … Improving the purity and credibility of the ever-increasing number of cell …

… entropy has also been used as the measurement, the definition as well as the computation of
our internal purity is … to use differential entropy to measure the purity of a cluster as well as a …

… hierarchical clustering and K-means clustering algorithms … is a vital factor in data clustering,
two measures are used in … metrics - entropy and purity - to assess the clustering quality. The …

… of entropy, this paper judges the randomness and disorder degree of an event by calculating
the entropy value, and judges the dispersion degree of an index by using the entropy value …

… K) denote the entropy of class distribution C and clustering K, … that a clustering is successful
if all clusters contain only data … meant a decrease in cluster purity, consistent with the theory …

… data stream clustering algorithms. Selected standard data stream clustering algorithms are
… index, Dunn index, entropy, F1 measure, purity and within cluster sum of square measure. …

… and partition entropy are measured to find the number of clusters … K-means clustering Clusters
with high purity and less … The optimal choice of number clusters shows improvement in the …

… In this paper, the impact of … can be investigated for measuring the performance of NPIR for
different distance measures. We can use evaluation measures besides the purity and entropy …

… It does not provide the unique clustering results, thus, we choose initial … The bigger value
of purity is an indication of good clustering. Entropy is a negative measurement. The lower …

… The experimental results were evaluated using three clustering metrics: NMI, ACC and
Purity. As shown in Table 2, the results demonstrate the performance of the algorithms. …