是對k-mean算法的數(shù)據(jù)分析處理,運(yùn)行時(shí)需輸入數(shù)據(jù),其中有參考數(shù)據(jù),希望對大家的學(xué)習(xí)有所幫助
標(biāo)簽： k-mean 數(shù)據(jù) 算法 數(shù)據(jù)分析
上傳時(shí)間： 2013-12-05
上傳用戶：ukuk
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(biāo)簽： the decision clusters Cluster
上傳時(shí)間： 2013-12-21
上傳用戶：gxmm
一個(gè)聚類算法用k-mean處理后迭代，論文發(fā)表在PAK
上傳時(shí)間： 2013-12-09
上傳用戶：66666
k-mean算法演示，利用手工輸入點(diǎn)通過k類自動聚合
上傳時(shí)間： 2016-04-09
上傳用戶：shanml
神經(jīng)網(wǎng)絡(luò)中的k-mean聚類算法，并在MFC中以主觀形式顯示學(xué)習(xí)過程。
標(biāo)簽： k-mean 神經(jīng)網(wǎng)絡(luò) 聚類算法
上傳時(shí)間： 2013-12-02
上傳用戶：chongcongying
分級聚類算法：包括k-mean max-dist min-dist 程序使用方法： 程序中打開文件“.dat”-》選擇聚類方法-》顯示數(shù)據(jù) .dat文件格式： 分成幾類 輸入樣本維數(shù) 樣本個(gè)數(shù) 下面依次為樣本特征向量
標(biāo)簽： dat max-dist min-dist k-mean
上傳時(shí)間： 2013-12-22
上傳用戶：alan-ee
一種c語言算法 k-mean.c
上傳時(shí)間： 2013-12-06
上傳用戶：頂?shù)弥?/p>
k-mean:經(jīng)典K均值算法,適用領(lǐng)域：語音識別，圖像識別
上傳時(shí)間： 2016-10-19
上傳用戶：evil
k-mean算法，并通過了IRIS數(shù)據(jù)的測試。
上傳時(shí)間： 2013-12-18
上傳用戶：shawvi
模式識別 k-mean算法程序，用Visual c++編寫
上傳時(shí)間： 2013-12-12
上傳用戶：1079836864
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