單純形法算法,int K,M,N,Q=100,Type,Get,Let,Et,Code[50],XB[50],IA,IAA[50],Indexg,Indexl,Indexe float Sum,A[50][50],B[50],C[50]
標(biāo)簽： 50 Indexg Indexe Indexl
上傳時(shí)間： 2013-12-22
上傳用戶：頂?shù)弥?/p>
移動(dòng)通信系統(tǒng)漫游規(guī)律研究及規(guī)律的應(yīng)用等,K
標(biāo)簽： 移動(dòng)通信系統(tǒng) 漫游
上傳時(shí)間： 2016-01-22
上傳用戶：familiarsmile
模式識(shí)別算法 k均值和感知器算法的具體實(shí)現(xiàn)實(shí)例
上傳時(shí)間： 2014-01-20
上傳用戶：stella2015
自己花費(fèi)整整兩個(gè)星期寫出來(lái)的關(guān)于數(shù)據(jù)挖掘K-means算法的論文，論文中詳細(xì)的進(jìn)行了論述并提出了改進(jìn)后的算法以及對(duì)比歡迎下載
標(biāo)簽： K-means 數(shù)據(jù)挖掘 算法 論文
上傳時(shí)間： 2016-01-25
上傳用戶：時(shí)代電子小智
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標(biāo)簽： lvbfdghfdhxfgjhkh gfjhgljl jhkjo vbn
上傳時(shí)間： 2016-01-25
上傳用戶：450976175
This directory contains code implementing the K-means algorithm. Source code may be found in KMEANS.CPP. Sample data isfound in KM2.DAT. The KMEANS program accepts input consisting of vectors and calculates the given number of cluster centers using the K-means algorithm. Output is directed to the screen.
標(biāo)簽： code implementing directory algorithm
上傳時(shí)間： 2014-01-15
上傳用戶：woshini123456
k-means是一種經(jīng)典的聚類算法，這是用java實(shí)現(xiàn)k-means的源碼，其中包括了測(cè)試數(shù)據(jù)文件
上傳時(shí)間： 2014-01-11
上傳用戶：lizhizheng88
模式識(shí)別中K均值算法的示例程序，可對(duì)一組數(shù)據(jù)分類并圖形輸出分類結(jié)果。
上傳時(shí)間： 2013-12-23
上傳用戶：yph853211
C#程序員參考手冊(cè).本書適合於需要C#語(yǔ)言和．NET Framework類庫(kù)的快速參考的程序員和希望通過(guò)代碼示例學(xué)習(xí)編程的程序員閱讀。
標(biāo)簽： Framework 程序 NET 手冊(cè)
上傳時(shí)間： 2013-12-02
上傳用戶：wendy15
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
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