Improvement of K Mean Clustering Algorithm Based on Density

eliminate the dependence on the initial cluster and the accuracy of clustering is improved. Keywords. Data mining; K mean algorithm; density; 

Ocean Front Reconstruction Method Based on K-Means Algorithm

Hybrid of K-means clustering and naive Bayes classifier for

Influence of an efficient Hierarchical Clustering Algorithm in

It may confuse the model that does not tend to provide better accuracy. Hence K-Means clustering is used to determine the size of cancer data for analysis [23] 

Improved k-Means Clustering Algorithm for Big Data Based on

11 ???. 2022 ?. and calculated based on the nearest centroid points which are random ... (KNN) and k-means clustering for predicting diagnostic accuracy.

Improved k-Means Clustering Algorithm for Big Data Based on

11 ???. 2022 ?. and calculated based on the nearest centroid points which are random ... (KNN) and k-means clustering for predicting diagnostic accuracy.

Improving the Accuracy and Efficiency of the k-means Clustering

3 ???. 2009 ?. Euclidean distance is generally considered to determine the distance between data points and the centroids. When all the points are included in ...

K-Splits: Improved K-Means Clustering Algorithm to Automatically

Reference [13] proposes a method of finding these centroids which leads to better accuracy. 3. Time consumption: Calculating the distance between all data 

SEGMENTATION OF SENTINEL SATELLITE IMAGES

TensorFlow Python libraries. Four cluster centers were defined for the K-Means algorithm. value (mIoU) and the Overall Accuracy (OA) were calculated.

Improving the Performance of K-Means Clustering For High

Hence to improve the efficiency and accuracy of mining task on high dimensional data the data must be preprocessed by efficient dimensionality reduction