How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.
標簽： the decision clusters Cluster
上傳時間： 2013-12-21
上傳用戶：gxmm
主要是KNN（the k-nearest neighbor algorithm ）,LVQ1（learning vector quantization 1）, DSM（decision surface mapping）算法。 and a simple clustering algorithm.
標簽： quantization k-nearest algorithm decision
上傳時間： 2016-02-07
上傳用戶：zhyiroy
這是一個關于K均值的聚類算法希望對大家有用
上傳時間： 2016-02-15
上傳用戶：ma1301115706
選擇第k小的元素，c語言 partition 要好好看看 理解函數意思
標簽： 元素
上傳時間： 2016-02-19
上傳用戶：ukuk
K-MEANS算法程序(MATLAB環境)
上傳時間： 2014-07-11
上傳用戶：JasonC
Linux Server Hacks, Volume Two By Brian K. Jones, William von Hagen ............................................... Publisher: O Reilly Pub Date: December 2005 Print ISBN-10: 0-596-10082-5 Print ISBN-13: 978-0-59-610082-7 Pages: 478
上傳時間： 2016-02-26
上傳用戶：ZJX5201314
加權k均值算法，或者稱為加權C均值聚類算法
上傳時間： 2016-02-26
上傳用戶：TF2015
這是我心儀已久的一本書 頂級大師Stanley B Lippman J o s é e L a j o i e合著的
上傳時間： 2014-01-01
上傳用戶：nanfeicui
k均值算法，基于兩維的
標簽： 均值算法
上傳時間： 2016-02-28
上傳用戶：cc1015285075
mnnnml , ,l, ,mk mmkkmlklmkkkkkkkkkkkkkm,mkl
標簽： mmkkmlklmkkkkkkkkkkkkkm mnnnml mkl mk
上傳時間： 2016-02-28
上傳用戶：yoleeson
