Algorithm Vision
λ¨Έμ‹ λŸ¬λ‹ μ•Œκ³ λ¦¬μ¦˜Intermediate

K-Means ν΄λŸ¬μŠ€ν„°λ§

데이터λ₯Ό k개의 ν΄λŸ¬μŠ€ν„°λ‘œ λΆ„ν• ν•˜λŠ” 기본적인 비지도 λ¨Έμ‹ λŸ¬λ‹ μ•Œκ³ λ¦¬μ¦˜μž…λ‹ˆλ‹€. 점을 κ°€μž₯ κ°€κΉŒμš΄ 쀑심에 ν• λ‹Ήν•˜κ³  쀑심 μœ„μΉ˜λ₯Ό 반볡적으둜 μ—…λ°μ΄νŠΈν•©λ‹ˆλ‹€. 고객 μ„ΈλΆ„ν™”, 이미지 μ••μΆ•, 탐색적 데이터 뢄석에 널리 μ‚¬μš©λ©λ‹ˆλ‹€.

#machine-learning#clustering#unsupervised#k-means++#data-science

Complexity Analysis

Time (Average)

O(n Γ— k Γ— i Γ— d)

Expected case performance

Space

O(n + k)

Memory requirements

Time (Best)

O(n Γ— k Γ— i Γ— d)

Best case performance

Time (Worst)

O(n Γ— k Γ— i Γ— d)

Worst case performance

11 data points

How it works

  • β€’ Partition n points into k clusters
  • β€’ Minimize within-cluster variance
  • β€’ Iterative algorithm
  • β€’ O(n Γ— k Γ— iterations) time complexity
  • β€’ Used in data mining and pattern recognition
Step: 1 / 0
500ms
SlowFast
Keyboard Shortcuts
Space Play/Pause← β†’ StepR Reset1-4 Speed

Real-time Statistics

Algorithm Performance Metrics

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Algorithm Visualization

Step 1 of 0

Initialize array to begin

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Code Execution

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Implementation

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K-Means Clustering - Algorithm Vision