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這次上傳的代碼是關(guān)于K-means clusters的代碼，希望能對大家有用。
上傳時間： 2013-12-15
上傳用戶：lindor
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
上傳時間： 2013-12-21
上傳用戶：gxmm
The last step in training phase is refinement of the clusters found above. Although DynamicClustering counters all the basic k-means disadvantages, setting the intra-cluster similarity r may require experimentation. Also, a cluster may have a lot in common with another, i.e., sequences assigned to it are as close to it as they are to another cluster. There may also be denser sub-clusters within the larger ones.
標(biāo)簽： DynamicClusteri refinement Although clusters
上傳時間： 2014-01-04
上傳用戶：watch100
Determination of number of clusters in K-Means Clustering and Application in color image segmenta
標(biāo)簽： Determination Application Clustering clusters
上傳時間： 2013-12-05
上傳用戶：zsjzc
High.Performance.Linux.clusters.With.Oscar.Rocks.openmosix.And.Mpi.2004--介紹linux集群的好書（英文版），希望大家喜歡
標(biāo)簽： Performance openmosix clusters Linux
上傳時間： 2016-10-09
上傳用戶：gyq
clustering matlab code,check the number of clusters alive at certain iterations
標(biāo)簽： clustering iterations clusters certain
上傳時間： 2013-12-15
上傳用戶：亞亞娟娟123
To identify distinguishable clusters of data in an n-dimensional pixel value image. Given: Samples of multi-spectral satellite images
標(biāo)簽： distinguishable n-dimensional identify clusters
上傳時間： 2017-08-08
上傳用戶：it男一枚
function [clusters,c,F]=fisher_classify(A,B,data) fisher判別法程序 輸入A、B為已知類別樣本的樣本-變量矩陣，data為待分類樣本 輸出C為判別系數(shù)向量
標(biāo)簽： fisher_classify function clusters fisher
上傳時間： 2013-12-19
上傳用戶：CHINA526
My version of k-means function. Improved so that there are no empty clusters after segmentation.
標(biāo)簽： segmentation Improved function clusters
上傳時間： 2013-12-24
上傳用戶：hoperingcong
二維的DBSCAN聚類算法，輸入（x,y）數(shù)組，搜索半徑Eps，密度搜索參數(shù)Minpts。輸出: clusters，每一行代表一個簇，形式為簇的對象對應(yīng)的原數(shù)據(jù)集的ID
上傳時間： 2015-06-01
上傳用戶：sy_jiadeyi
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