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K-meanCluster
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. (How the K-mean Cluster workStep 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 (Nk) 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.)
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- 积分:1
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dsp-shiyan
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simstruc_types
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AGA
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matlab
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- 积分:1
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DC_Motor_Driver_Block_RPi
DC Motor Driver Block using Soft PWM of WiringPi Library
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FLch5RMLeg2
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waveletthreshod
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- 2010-05-07 21:49:36下载
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signal-processing
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ECGcode
说明: 自己写的利用形态滤波方法去ECG基线漂移,附MIT-BIH心电数据库(受上传文件大小所限,只包含第124号数据供程序验证)(Written by myself, ECG baseline drift is removed by morphological filtering method, with MIT-BIH ECG database (limited by the size of uploaded files, only data No. 124 is included for program verification))
- 2020-06-19 23:00:02下载
- 积分:1