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  • Introduction
  • Machine Learning
    • Recommended Courses
      • For Undergrad Research
      • Math for Machine Learning
    • ML Notes
      • Covariance Correlation
      • Feature Selection
      • Linear Regression
      • Entropy, Cross-Entropy, KL Divergence
      • Bayesian Classifier
        • Terminology Review
        • Bayesian Classifier for Normally Distributed classes
      • Linear Discriminant Analysis
      • Logistic Regression
        • Logistic Regression Math
      • Logistic Regression-MaximumLikelihood
      • SVM
        • SVM concept
        • SVM math
      • Cross Validation
      • Parameter, Density Estimation
        • MAP, MLE
        • Gaussian Mixture Model
      • E-M
      • Density Estimation(non-parametric)
      • Unsupervised Learning
      • Clustering
      • kNN
      • WaveletTransform
      • Decision Tree
    • Probability and Statistics for Machine Learning
      • Introduction
      • Basics of Data Analysis
      • Probability for Discrete Random Variable
      • Poisson Distribution
      • Chi-Square Distribution
      • P-value and Statistical Hypothesis
      • Power and Sample Size
      • Hypothesis Test Old
      • Hypothesis Test
      • Multi Armed Bandit
      • Bayesian Inference
      • Bayesian Updating with Continuous Priors
      • Discrete Distribution
      • Comparison of Bayesian and frequentist inference
      • Confidence Intervals for Normal Data
      • Frequenist Methods
      • Null Hypothesis Significance Testing
      • Confidence Intervals: Three Views
      • Confidence Intervals for the Mean of Non-normal Data
      • Probabilistic Prediction
  • Industrial AI
    • PHM Dataset
    • BearingFault_Journal
      • Support Vector Machine based
      • Autoregressive(AR) model based
      • Envelope Extraction based
      • Wavelet Decomposition based
      • Prediction of RUL with Deep Convolution Nueral Network
      • Prediction of RUL with Information Entropy
      • Feature Model and Feature Selection
    • TempCore Journal
      • Machine learning of mechanical properties of steels
      • Online prediction of mechanical properties of hot rolled steel plate using machine learning
      • Prediction and Analysis of Tensile Properties of Austenitic Stainless Steel Using Artificial Neural
      • Tempcore, new process for the production of high quality reinforcing
      • TEMPCORE, the most convenient process to produce low cost high strength rebars from 8 to 75 mm
      • Experimental investigation and simulation of structure and tensile properties of Tempcore treated re
    • Notes
  • LiDAR
    • Processing of Point Cloud
    • Intro. 3D Object Detection
    • PointNet
    • PointNet++
    • Frustrum-PointNet
    • VoxelNet
    • Point RCNN
    • PointPillars
    • LaserNet
  • Simulator
    • Simulator List
    • CARLA
    • Airsim
      • Setup
      • Tutorial
        • T#1
        • T#2
        • T#3: Opencv CPP
        • T#4: Opencv Py
        • Untitled
        • T#5: End2End Driving
  • Resources
    • Useful Resources
    • Github
    • Jekyll
  • Reinforcement Learning
    • RL Overview
      • RL Bootcamp
      • MIT Deep RL
    • Textbook
    • Basics
    • Continuous Space RL
  • Unsupervised Learning
    • Introduction
  • Unclassified
    • Ethics
    • Conference Guideline
  • FPGA
    • Untitled
  • Numerical Method
    • NM API reference
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  • K-means
  • Optimization of K-Means
  • How to choose k ?
  • Referene

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  1. Machine Learning
  2. ML Notes

Clustering

PreviousUnsupervised LearningNextkNN

Last updated 3 years ago

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K-means

Optimization of K-Means

How to choose k ?

The value of k, the number of clusters, is a hyperparameter that has to be tuned by the data analyst. There are some techniques for selecting k. None of them is proven optimal. Most of those techniques require the analyst to make an “educated guess” by looking at some metrics or by examining cluster assignments visually.

Methods

  • Prediction Strength (See 9.2.3 [1] )

  • Gap statistic method

  • Elbow method

  • Average silihouette method

Referene

[2] Machine Learning, Huang, VTech

[1] The Hundred-Page Machine Learning Book

http://themlbook.com/wiki/doku.php
100 pages machine learning