有效地把相似的数字相加 [英] Efficiently grouping similar numbers together

问题描述

可能重复：
  维数阵列集群

我有数字数组像 [1，20，300，45，5，60，10，270，3] 。什么是一个高效的算法基于接近分组这些数字相加？在这种情况下，我期望像 [1，3，5] ， [20，45，60] 和 [270，300] 。

I have an array of numbers like [1, 20, 300, 45, 5, 60, 10, 270, 3]. What is an efficient algorithm for grouping these numbers together based on proximity? In this case I'd expect something like [1, 3, 5], [20, 45, 60] and [270, 300].

推荐答案

你问的最难的是如何真正界定接近。你会期望的输出从 [5,10,15,20] ？难道是相同的分组为 [500,1000,1500,2000] ？

The hardest part of what you are asking is how to actually define proximity. What would you expect the output to be from [5,10,15,20]? Would it be the same groupings as for [500,1000,1500,2000]?

什么 [1,2,3,5,7,8,9] ？如果有一组或三个？ （或两个？）。
那么 [1,2,3,5,7,8,9,1075,4000] ？难道1075和4000成为组合在一起？难道较小的号码分组越来越受到较大的数字样本的变化？

What about [1,2,3,5,7,8,9]? Should there be one group or three? (or two?).
What about [1,2,3,5,7,8,9,1075,4000]? Do 1075 and 4000 become grouped together? Does the groupings of the smaller numbers become changed by the larger numbers in the sample?

这个问题是问的东西通过机器学习的整个领域：聚类分析 也许这<一href="http://stackoverflow.com/questions/6147466/what-clustering-algorithm-to-use-on-1-d-data">related问题将帮助？

This question is something asked by an entire field of Machine Learning: Cluster Analysis Perhaps this related question will help?

我想你想要的是 K-均值聚类（有益链接到相关的问题），但你需要知道你要多少组将数据分割成使用它。

I think what you want is K-means clustering (helpfully linked to in the related question), but you need to know how many groups you want to split your data into to use it.

这篇关于有效地把相似的数字相加的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！