What is the primary objective of the K-means clustering algorithm in data analysis?
The K-means clustering algorithm is an unsupervised learning technique used in data analysis to partition a dataset into K distinct clusters. The primary objective of K-means is to group data points into clusters where each data point belongs to the cluster with the nearest mean (centroid), which minimizes the within-cluster variance.
(?)2 + 3.113 = 22.92 – 61.03
(15.98% of 399.99) - 6.998 = √?
(124.99)² = ?
64.889% of 399.879 + √? = 54.90% of 799.80 – 44.03% of 400.21
70.008% of 399.98 + ?% of 399.999 = 80.105% of 599.998
? × 32.91 – 847.95 ÷ √16.4 – 13.982 = √24.7 × 24.04
24.11% of 249.99 + √143.97 ÷ 12.02 = ?
√65 of 14.97 + √50 = (12.02)2 - ?