# heuristic algorithm

Published articles for heuristic algorithm.

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## k-Means Clustering and Birth Rates

DevFeed: [k-Means Clustering and Birth Rates](<https://devfeed.tech/articles/k-means-clustering-and-birth-rates-40301.md>)

Original publisher: [Read original article](<https://www.jeremykun.com/2013/02/04/k-means-clustering-and-birth-rates/>)

Published: 2013-02-04T17:54:20Z

Content type: tutorial

Language: en

Sources: [Jeremy Kun](<https://devfeed.tech/sources/jeremy-kun.md>)

Topics: [clustering](<https://devfeed.tech/topics/clustering.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>), [Mathematics](<https://devfeed.tech/topics/mathematics.md>)

Tags: [algorithm](<https://devfeed.tech/tags/algorithm.md>), [clustering](<https://devfeed.tech/tags/clustering.md>), [heuristic-algorithm](<https://devfeed.tech/tags/heuristic-algorithm.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [mathematica](<https://devfeed.tech/tags/mathematica.md>), [mathematics](<https://devfeed.tech/tags/mathematics.md>), [metrics](<https://devfeed.tech/tags/metrics.md>), [partition](<https://devfeed.tech/tags/partition.md>), [programming](<https://devfeed.tech/tags/programming.md>), [python](<https://devfeed.tech/tags/python.md>), [unsupervised-learning](<https://devfeed.tech/tags/unsupervised-learning.md>)

### AI overview

This tutorial introduces the clustering problem, formalizes k-means clustering as a partitioning problem over points in a metric space, explains why finding an exact solution is difficult, and describes using a heuristic algorithm instead.

### Source excerpt

A common problem in machine learning is to take some kind of data and break it up into "clumps" that best reflect how the data is structured. A set of points which are all collectively close to each other should be in the same clump. A simple picture will clarify any vagueness in this: cluster-example Here the data consists of points in the plane. There is an obvious clumping of the data into three pieces, and we want a way to automatically determine which points are in which clumps.