In fuzzy cluster analysis, many algorithms have been developed. In general, the most widely used is the Fuzzy c-Means Algorithms (FCMA).
標(biāo)簽： algorithms developed analysis cluster
上傳時(shí)間： 2013-12-04
上傳用戶：阿四AIR
linux oracle10g cluster
標(biāo)簽： cluster oracle linux 10g
上傳時(shí)間： 2013-12-30
上傳用戶：ukuk
The Linux Enterprise cluster explains how to take a number of inexpensive computers with limited resources, place them on a normal computer network, and install free software so that the computers act together like one powerful server. This makes it possible to build a very inexpensive and reliable business system for a small business or a large corporation. The book includes information on how to build a high-availability server pair using the Heartbeat package, how to use the Linux Virtual Server load balancing software, how to configure a reliable printing system in a Linux cluster environment, and how to build a job scheduling system in Linux with no single point of failure. The book also includes information on high availability techniques that can be used with or without a cluster, making it helpful for System Administrators even if they are not building a cluster. Anyone interested in deploying Linux in an environment where low cost computer reliability is important will find this book useful.
標(biāo)簽： inexpensive Enterprise computers explains
上傳時(shí)間： 2014-11-29
上傳用戶：zhangliming420
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
kmeans算法實(shí)現(xiàn) a simple k-means clustering routine. returns the cluster labels of the data points in an array.
標(biāo)簽： clustering the k-means cluster
上傳時(shí)間： 2013-12-28
上傳用戶：一諾88
KMEANS Trains a k means cluster model.CENTRES = KMEANS(CENTRES, DATA, OPTIONS) uses the batch K-means algorithm to set the centres of a cluster model. The matrix DATA represents the data which is being clustered, with each row corresponding to a vector. The sum of squares error function is used. The point at which a local minimum is achieved is returned as CENTRES.
標(biāo)簽： CENTRES KMEANS OPTIONS cluster
上傳時(shí)間： 2014-01-07
上傳用戶：zhouli
pMatlab is a toolsbox from MIT for running matlab in parallel style on a multi-core PC or a cluster environment. These two documents summary the usage of pMatlab and running time measurements on three simple Monte Carlo simulation codes.
標(biāo)簽： multi-core toolsbox parallel pMatlab
上傳時(shí)間： 2014-12-05
上傳用戶：zhliu007
Demo HZ256 cluster LCD2 CW31 SH v1 ICD
標(biāo)簽： cluster Demo LCD2 256
上傳時(shí)間： 2013-12-10
上傳用戶：181992417
MS-clustering is designed to rapidly cluster large MS/MS datasets. The program merges similar spectra (having similar m/z values ?within a given tolerance), and creates a single consensus spectrum as a representative. The input formats accepted are: dta, mgf, mzXML. The output format is mgf.
標(biāo)簽： MS-clustering designed datasets cluster
上傳時(shí)間： 2013-12-20
上傳用戶：cursor
前面我們通過Veritas cluster Server for DB2雙機(jī)-入門一文已經(jīng)向大家介紹了DB2雙機(jī)的基本原理和配置方法，本文將接續(xù)上文，繼續(xù)介紹DB2的高級需求－大規(guī)模并行處理（Massively Parallel Processing, MPP）－環(huán)境下，用戶如何利用VCS配置雙機(jī)互備環(huán)境。
標(biāo)簽： DB2 Veritas cluster Server
上傳時(shí)間： 2014-09-04
上傳用戶：libinxny
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