# K-Means Hyperparameters
<a name="k-means-api-config"></a>

In the [https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_CreateTrainingJob.html](https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_CreateTrainingJob.html) request, you specify the training algorithm that you want to use. You can also specify algorithm-specific hyperparameters as string-to-string maps. The following table lists the hyperparameters for the k-means training algorithm provided by Amazon SageMaker AI. For more information about how k-means clustering works, see [How K-Means Clustering Works](algo-kmeans-tech-notes.md).


| Parameter Name | Description | 
| --- | --- | 
| feature\$1dim | The number of features in the input data. **Required** Valid values: Positive integer  | 
| k |  The number of required clusters. **Required** Valid values: Positive integer  | 
| epochs | The number of passes done over the training data. **Optional** Valid values: Positive integer Default value: 1  | 
| eval\$1metrics | A JSON list of metric types used to report a score for the model. Allowed values are `msd` for Means Square Deviation and `ssd` for Sum of Square Distance. If test data is provided, the score is reported for each of the metrics requested. **Optional** Valid values: Either `[\"msd\"]` or `[\"ssd\"]` or `[\"msd\",\"ssd\"]` . Default value: `[\"msd\"]`  | 
| extra\$1center\$1factor | The algorithm creates K centers = `num_clusters` \$1 `extra_center_factor` as it runs and reduces the number of centers from K to `k` when finalizing the model. **Optional** Valid values: Either a positive integer or `auto`. Default value: `auto`  | 
| half\$1life\$1time\$1size | Used to determine the weight given to an observation when computing a cluster mean. This weight decays exponentially as more points are observed. When a point is first observed, it is assigned a weight of 1 when computing the cluster mean. The decay constant for the exponential decay function is chosen so that after observing `half_life_time_size` points, its weight is 1/2. If set to 0, there is no decay. **Optional** Valid values: Non-negative integer Default value: 0  | 
| init\$1method | Method by which the algorithm chooses the initial cluster centers. The standard k-means approach chooses them at random. An alternative k-means\$1\$1 method chooses the first cluster center at random. Then it spreads out the position of the remaining initial clusters by weighting the selection of centers with a probability distribution that is proportional to the square of the distance of the remaining data points from existing centers. **Optional** Valid values: Either `random` or `kmeans++`. Default value: `random`  | 
| local\$1lloyd\$1init\$1method | The initialization method for Lloyd's expectation-maximization (EM) procedure used to build the final model containing `k` centers. **Optional** Valid values: Either `random` or `kmeans++`. Default value: `kmeans++`  | 
| local\$1lloyd\$1max\$1iter | The maximum number of iterations for Lloyd's expectation-maximization (EM) procedure used to build the final model containing `k` centers. **Optional** Valid values: Positive integer Default value: 300  | 
| local\$1lloyd\$1num\$1trials | The number of times the Lloyd's expectation-maximization (EM) procedure with the least loss is run when building the final model containing `k` centers. **Optional** Valid values: Either a positive integer or `auto`. Default value: `auto`  | 
| local\$1lloyd\$1tol | The tolerance for change in loss for early stopping of Lloyd's expectation-maximization (EM) procedure used to build the final model containing `k` centers. **Optional** Valid values: Float. Range in [0, 1]. Default value: 0.0001  | 
| mini\$1batch\$1size | The number of observations per mini-batch for the data iterator. **Optional** Valid values: Positive integer Default value: 5000  |