{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Serafeim Loukas, PhD",
  "title_listed": "K-Means Clustering: How It Works & Finding The Optimum Number Of Clusters In The Data",
  "keywords": "",
  "domain": "机器学习",
  "content_type": "深度技术解析",
  "is_paid_flag": "否",
  "url": "https://towardsdatascience.com/k-means-clustering-how-it-works-finding-the-optimum-number-of-clusters-in-the-data-13d18739255c/",
  "publish_date": "2020-09-09",
  "corpus_stage": "",
  "notes": "",
  "id": "b09a237c0bfe",
  "collected_at": "2026-10-02T08:12:02",
  "title": "K-Means Clustering: How It Works & Finding The Optimum Number Of Clusters In The Data | Towards Data Science",
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It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# K-Means Clustering: How It Works & Finding The Optimum Number Of Clusters In The Data\nMathematical formulation, Finding the optimum number of clusters and a working example in Python.\n[Serafeim Loukas, PhD](https://towardsdatascience.com/author/seralouk/)\nSeptember 9, 20208 min read\n### Mathematical formulation, Finding the optimum number of clusters and a working example in Python\n![Image created by the author](../images/005.png)\nImage created by the author\n* * *\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps.\n## Introduction\n> **K-means is one of the most widely used unsupervised clustering methods.**\nThe **K-means** algorithm clusters the data at hand by trying to separate samples into **K** groups of equal variance, minimizing a criterion known as the _**inertia**_ or **within-cluster sum-of-squares**. **This algorithm requires the number of clusters to be specified**. It scales well to large number of samples and has been used across a large range of application areas in many different fields.\nThe k-means algorithm divides a set of **N** samples (stored in a data matrix **X**) into **K** disjoint clusters **C** , each described by the mean _**μj**_ of the samples in the cluster. The means are commonly called the cluster \"**centroids\".**\n**K-means** algorithm falls into the family of **unsupervised** **machine** **learning** algorithms/methods. For this family of models, the research needs to have at hand a dataset with some observations **without** the need of having also the **labels** /**classes** of the observations. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data.\nNow let's discover the **mathematical foundations** of the algorithm.\n* * *\n  * **NEW** : After a great deal of hard work and staying behind the scenes for quite a while, we're excited to now offer our expertise through a platform, the \"[**Data Science Hub**](https://www.patreon.com/TheDataScienceHub\\))\" on Patreon (<https://www.patreon.com/TheDataScienceHub>). This hub is our way of providing you with **bespoke consulting services** and comprehensive **responses to all your inquiries** , ranging from Machine Learning to strategic data analytics planning.\n\n\n* * *\n## Mathematical formulation\nGiven a set of observations (**x** 1, **x** 2, ..., **x** _n_), where each observation is a _d_ -dimensional real vector, _k_ -means clustering aims to partition the _n_ observations into _k_ (≤ _n_) sets **S** = {_S_1, _S_2, ..., _Sk_} so as to minimize the within-cluster sum of squares (WCSS) (i.e. [variance](https://en.wikipedia.org/wiki/Variance)). Formally, the objective is defined as follows:\n![Latex image created by the author.](../images/001.png)\nLatex image created by the author.\nwhere _**μ**_ _i_ is the mean of points in group/cluster _Si_.\n* * *\nAnother resource. Learn Data Science and ML with the help of an 🤖 AI-powered tutor. Start here h[ttps://aigents.co/learn ](https://aigents.co/learn)choose a topic and he will show up where you need him. No paywall, no signups, no ads.\n* * *\n## Understanding the algorithm behind\n![Example of some Voronoi diagrams \\(source: https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_digits.html#sphx-glr-auto-examples-cluster-plot-kmeans-digits-py\\).](../images/002.png)\nExample of some Voronoi diagrams (source: <https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_digits.html#sphx-glr-auto-examples-cluster-plot-kmeans-digits-py>).\nThe algorithm can also be understood through the concept of Voronoi diagrams.\nFirst, the Voronoi diagram of the points is calculated using the current centroids. Initially, the centroids are _usually_ chosen randomly but this depends on the underlying package / library / software used.\nEach segment in the Voronoi diagram becomes a separate cluster.\nSecondly, the centroids are updated to the mean of each segment. The algorithm then repeats this until a stopping criterion is fulfilled.\nThe algorithm **stops** when the relative **decrease** in the **objective** **function** between iterations is **less** than the given **tolerance** (ε) value **or** when **centroids** **move** (in space) **less** than the **tolerance**.\n* * *\n## Finding the optimum number of clusters\nAs stated in the introduction, **the K-Means algorithm requires the number of clusters to be specified beforehand.** In case that there is no valid assumption about the underlying number of clusters in the data, this task can be difficult to tackle.\nFortunately, there are some methods for estimating the optimum number of clusters in our data such as the **Silhouette Coefficient or the Elbow method.** If the ground truth labels are not known, evaluation must be performed using the model itself. In this article we will only use the **Silhouette Coefficient** and not the Elbow method which is much simpler. Briefly, the [elbow method](https://en.wikipedia.org/wiki/Elbow_method_\\(clustering\\)) looks at the percentage of variance explained as a function of the number of clusters: One should choose a number of clusters so that adding another cluster doesn't give much better modeling of the data.\n**A higher Silhouette Coefficient score relates to a model with better defined clusters**. The Silhouette Coefficient is defined for each sample and is composed of two scores:\n  * **a** : The mean distance between a sample and all other points in the same class.\n  * **b** : The mean distance between a sample and all other points in the _next nearest cluster_.\n\n\nThe Silhouette Coefficient _**s**_ **for a single sample** is then defined as:\n![Latex image created by the author.](../images/003.png)\nLatex image created by the author.\n**Finally, the Total Silhouette Coefficient for a** _**set of samples**_**is given as the mean of the Silhouette Coefficient for each sample.**\n> The best value is 1 and the worst value is -1. Values near 0 indicate overlapping clusters. Negative values generally indicate that a sample has been assigned to the wrong cluster, as a different cluster is more similar.\n* * *\n## Python working example\nFor this example we will create artificial data i.e. artificial clusters. This way **we will know in advance** the **ground** **through** i.e. the **exact number of clusters in our dataset**.\nLet's start with importing the required python libraries:\ntext\n\n```\nfrom sklearn.datasets import make_blobs\nfrom sklearn.cluster import KMeans\nfrom sklearn.metrics import silhouette_samples, silhouette_score\n\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\nimport numpy as np\n```\n\nNext, let's create some artificial data containing **500 samples** , **2 features/variables** , and **K=4 clusters**.\ntext\n\n```\n# Generating the data\n# This particular setting has one distinct cluster and 3 clusters placed close together.\nX, y = make_blobs(n_samples=500,\n                  n_features=2,\n                  centers=4,\n                  cluster_std=1,\n                  center_box=(-10.0, 10.0),\n                  shuffle=True,\n                  random_state=1)\n```\n\nWe know that we have K=4 clusters in the data however, in order to understand how the Silhouette Score works **we will fit the model using a range of different number of clusters.**\nEach time, we will estimate the **Silhouette Score** and also plot the data with the final (**converged**) **centroids**. All these are done by the following code:\ntext\n\n```\nrange_n_clusters = [3, 4, 5]\n```\n\ntext\n\n```\nfor n_clusters in range_n_clusters:\n    # Create a subplot with 1 row and 2 columns\n    fig, (ax1, ax2) = plt.subplots(1, 2)\n    fig.set_size_inches(18, 7)\n\n    # The 1st subplot is the silhouette plot\n    # The silhouette coefficient can range from -1, 1 but in this example all\n    # lie within [-0.1, 1]\n    ax1.set_xlim([-0.1, 1])\n    # The (n_clusters+1)*10 is for inserting blank space between silhouette\n    # plots of individual clusters, to demarcate them clearly.\n    ax1.set_ylim([0, len(X) + (n_clusters + 1) * 10])\n\n    # Initialize the clusterer with n_clusters value and a random generator\n    # seed of 10 for reproducibility.\n    clusterer = KMeans(n_clusters=n_clusters, random_state=10)\n    cluster_labels = clusterer.fit_predict(X)\n\n    # The silhouette_score gives the average value for all the samples.\n    # This gives a perspective into the density and separation of the formed\n    # clusters\n    silhouette_avg = silhouette_score(X, cluster_labels)\n    print(\"For n_clusters =\", n_clusters,\n          \"The average silhouette_score is :\", silhouette_avg)\n\n    # Compute the silhouette scores for each sample\n    sample_silhouette_values = silhouette_samples(X, cluster_labels)\n\n    y_lower = 10\n    for i in range(n_clusters):\n        # Aggregate the silhouette scores for samples belonging to\n        # cluster i, and sort them\n        ith_cluster_silhouette_values = \n            sample_silhouette_values[cluster_labels == i]\n\n        ith_cluster_silhouette_values.sort()\n\n        size_cluster_i = ith_cluster_silhouette_values.shape[0]\n        y_upper = y_lower + size_cluster_i\n\n        color = cm.nipy_spectral(float(i) / n_clusters)\n        ax1.fill_betweenx(np.arange(y_lower, y_upper),\n                          0, ith_cluster_silhouette_values,\n                          facecolor=color, edgecolor=color, alpha=0.7)\n\n        # Label the silhouette plots with their cluster numbers at the middle\n        ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n\n        # Compute the new y_lower for next plot\n        y_lower = y_upper + 10  # 10 for the 0 samples\n\n    ax1.set_title(\"The silhouette plot for the various clusters.\")\n    ax1.set_xlabel(\"The silhouette coefficient values\")\n    ax1.set_ylabel(\"Cluster label\")\n\n    # The vertical line for average silhouette score of all the values\n    ax1.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n\n    ax1.set_yticks([])  # Clear the yaxis labels / ticks\n    ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n\n    # 2nd Plot showing the actual clusters formed\n    colors = cm.nipy_spectral(cluster_labels.astype(float) / n_clusters)\n    ax2.scatter(X[:, 0], X[:, 1], marker='.', s=30, lw=0, alpha=0.7,\n                c=colors, edgecolor='k')\n\n    # Labeling the clusters\n    centers = clusterer.cluster_centers_\n    # Draw white circles at cluster centers\n    ax2.scatter(centers[:, 0], centers[:, 1], marker='o',\n                c=\"white\", alpha=1, s=200, edgecolor='k')\n\n    for i, c in enumerate(centers):\n        ax2.scatter(c[0], c[1], marker='$%d$' % i, alpha=1,\n                    s=50, edgecolor='k')\n\n    ax2.set_title(\"The visualization of the clustered data.\")\n    ax2.set_xlabel(\"Feature space for the 1st feature\")\n    ax2.set_ylabel(\"Feature space for the 2nd feature\")\n\n    plt.suptitle((\"Silhouette analysis for KMeans clustering on sample data \"\n                  \"with n_clusters = %d\" % n_clusters),\n                 fontsize=14, fontweight='bold')\n\nplt.show()\n```\n\n![Code output in the console \\(image created by the author\\)](../images/004.png)\nCode output in the console (image created by the author)\n> We observe that the average/mean **Silhouette Score is the highest in the case of K=4 clusters.**\n**This verifies that the Silhouette Score is a good measure of K-means fitting goodness.**\nWe also produced these Figures:\n![Silhouette score &amp; the data with the final \\(converged\\) centroids \\(image created by the author\\)](../images/005.png)\nSilhouette score & the data with the final (**converged**) **centroids** (image created by the author)\n![Silhouette score &amp; the data with the final \\(converged\\) centroids \\(image created by the author\\)](../images/006.png)\nSilhouette score & the data with the final (**converged**) **centroids** (image created by the author)\n![Silhouette score &amp; the data with the final \\(converged\\) centroids \\(image created by the author\\)](../images/007.png)\nSilhouette score & the data with the final (**converged**) **centroids** (image created by the author)\n**The vertical line is the average silhouette score of all the values.**\n**Again, we can also visually verify that the Silhouette Score is a good measure of K-means fitting goodness for the specific example.**\n* * *\n## **Conclusions**\n**K-means** is one of the most widely used unsupervised clustering methods. The algorithm clusters the data at hand by trying to separate samples into **K** groups of equal variance, minimizing a criterion known as the inertia or within-cluster sum-of-squares. This algorithm requires the number of clusters to be specified.\nHowever, if the ground truth labels are not known, the evaluation must be performed using the model itself.\nIn this article we used the **Silhouette Coefficient.** From the working example, we can verify that the Silhouette Score is a good measure of K-means fitting goodness.\n* * *\nThat's all! Hope you liked this new article - more to come soon!\n* * *\n## References\n[1] Peter J. Rousseeuw (1987). \"Silhouettes: a Graphical Aid to the Interpretation and Validation of Cluster Analysis\". Computational and Applied Mathematics 20: 53–65. [doi:10.1016/0377–0427(87)90125–7](https://doi.org/10.1016/0377-0427\\(87\\)90125-7).\n[2] <https://en.wikipedia.org/wiki/Silhouette_(clustering)>\n* * *\n### You may also like:\n> [**LSTM Time-Series Forecasting: Predicting Stock Prices Using An LSTM Model**](https://towardsdatascience.com/lstm-time-series-forecasting-predicting-stock-prices-using-an-lstm-model-6223e9644a2f)\n> [**Time-Series Forecasting: Predicting Stock Prices Using An ARIMA Model**](https://towardsdatascience.com/time-series-forecasting-predicting-stock-prices-using-an-arima-model-2e3b3080bd70)\n* * *\n## Stay tuned & support this effort\nIf you liked and found this article useful, **follow** me to be able to see all my new posts.\nQuestions? 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It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# K-Means Clustering: How It Works & Finding The Optimum Number Of Clusters In The Data\nMathematical formulation, Finding the optimum number of clusters and a working example in Python.\n[Serafeim Loukas, PhD](https://towardsdatascience.com/author/seralouk/)\nSeptember 9, 20208 min read\n### Mathematical formulation, Finding the optimum number of clusters and a working example in Python\n![Image created by the author](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/09/1lh-a75nwg8gi_aMGcaQkSA.png)\nImage created by the author\n* * *\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps.\n## Introduction\n> **K-means is one of the most widely used unsupervised clustering methods.**\nThe **K-means** algorithm clusters the data at hand by trying to separate samples into **K** groups of equal variance, minimizing a criterion known as the _**inertia**_ or **within-cluster sum-of-squares**. **This algorithm requires the number of clusters to be specified**. It scales well to large number of samples and has been used across a large range of application areas in many different fields.\nThe k-means algorithm divides a set of **N** samples (stored in a data matrix **X**) into **K** disjoint clusters **C** , each described by the mean _**μj**_ of the samples in the cluster. The means are commonly called the cluster \"**centroids\".**\n**K-means** algorithm falls into the family of **unsupervised** **machine** **learning** algorithms/methods. For this family of models, the research needs to have at hand a dataset with some observations **without** the need of having also the **labels** /**classes** of the observations. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data.\nNow let's discover the **mathematical foundations** of the algorithm.\n* * *\n  * **NEW** : After a great deal of hard work and staying behind the scenes for quite a while, we're excited to now offer our expertise through a platform, the \"[**Data Science Hub**](https://www.patreon.com/TheDataScienceHub\\))\" on Patreon (<https://www.patreon.com/TheDataScienceHub>). This hub is our way of providing you with **bespoke consulting services** and comprehensive **responses to all your inquiries** , ranging from Machine Learning to strategic data analytics planning.\n\n\n* * *\n## Mathematical formulation\nGiven a set of observations (**x** 1, **x** 2, ..., **x** _n_), where each observation is a _d_ -dimensional real vector, _k_ -means clustering aims to partition the _n_ observations into _k_ (≤ _n_) sets **S** = {_S_1, _S_2, ..., _Sk_} so as to minimize the within-cluster sum of squares (WCSS) (i.e. [variance](https://en.wikipedia.org/wiki/Variance)). Formally, the objective is defined as follows:\n![Latex image created by the author.](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/09/14Fo_dM0Pt0c_DBi39CENog.png)\nLatex image created by the author.\nwhere _**μ**_ _i_ is the mean of points in group/cluster _Si_.\n* * *\nAnother resource. Learn Data Science and ML with the help of an 🤖 AI-powered tutor. Start here h[ttps://aigents.co/learn ](https://aigents.co/learn)choose a topic and he will show up where you need him. No paywall, no signups, no ads.\n* * *\n## Understanding the algorithm behind\n![Example of some Voronoi diagrams \\(source: https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_digits.html#sphx-glr-auto-examples-cluster-plot-kmeans-digits-py\\).](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/09/1HAm9e3z7S9LGx0VVhsNq5w.png)\nExample of some Voronoi diagrams (source: <https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_digits.html#sphx-glr-auto-examples-cluster-plot-kmeans-digits-py>).\nThe algorithm can also be understood through the concept of Voronoi diagrams.\nFirst, the Voronoi diagram of the points is calculated using the current centroids. Initially, the centroids are _usually_ chosen randomly but this depends on the underlying package / library / software used.\nEach segment in the Voronoi diagram becomes a separate cluster.\nSecondly, the centroids are updated to the mean of each segment. The algorithm then repeats this until a stopping criterion is fulfilled.\nThe algorithm **stops** when the relative **decrease** in the **objective** **function** between iterations is **less** than the given **tolerance** (ε) value **or** when **centroids** **move** (in space) **less** than the **tolerance**.\n* * *\n## Finding the optimum number of clusters\nAs stated in the introduction, **the K-Means algorithm requires the number of clusters to be specified beforehand.** In case that there is no valid assumption about the underlying number of clusters in the data, this task can be difficult to tackle.\nFortunately, there are some methods for estimating the optimum number of clusters in our data such as the **Silhouette Coefficient or the Elbow method.** If the ground truth labels are not known, evaluation must be performed using the model itself. In this article we will only use the **Silhouette Coefficient** and not the Elbow method which is much simpler. Briefly, the [elbow method](https://en.wikipedia.org/wiki/Elbow_method_\\(clustering\\)) looks at the percentage of variance explained as a function of the number of clusters: One should choose a number of clusters so that adding another cluster doesn't give much better modeling of the data.\n**A higher Silhouette Coefficient score relates to a model with better defined clusters**. The Silhouette Coefficient is defined for each sample and is composed of two scores:\n  * **a** : The mean distance between a sample and all other points in the same class.\n  * **b** : The mean distance between a sample and all other points in the _next nearest cluster_.\n\n\nThe Silhouette Coefficient _**s**_ **for a single sample** is then defined as:\n![Latex image created by the author.](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/09/1oDDBGQuKAIIMdWBsc7NVrg.png)\nLatex image created by the author.\n**Finally, the Total Silhouette Coefficient for a** _**set of samples**_**is given as the mean of the Silhouette Coefficient for each sample.**\n> The best value is 1 and the worst value is -1. Values near 0 indicate overlapping clusters. Negative values generally indicate that a sample has been assigned to the wrong cluster, as a different cluster is more similar.\n* * *\n## Python working example\nFor this example we will create artificial data i.e. artificial clusters. This way **we will know in advance** the **ground** **through** i.e. the **exact number of clusters in our dataset**.\nLet's start with importing the required python libraries:\ntext\n\n```\nfrom sklearn.datasets import make_blobs\nfrom sklearn.cluster import KMeans\nfrom sklearn.metrics import silhouette_samples, silhouette_score\n\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\nimport numpy as np\n```\n\nNext, let's create some artificial data containing **500 samples** , **2 features/variables** , and **K=4 clusters**.\ntext\n\n```\n# Generating the data\n# This particular setting has one distinct cluster and 3 clusters placed close together.\nX, y = make_blobs(n_samples=500,\n                  n_features=2,\n                  centers=4,\n                  cluster_std=1,\n                  center_box=(-10.0, 10.0),\n                  shuffle=True,\n                  random_state=1)\n```\n\nWe know that we have K=4 clusters in the data however, in order to understand how the Silhouette Score works **we will fit the model using a range of different number of clusters.**\nEach time, we will estimate the **Silhouette Score** and also plot the data with the final (**converged**) **centroids**. All these are done by the following code:\ntext\n\n```\nrange_n_clusters = [3, 4, 5]\n```\n\ntext\n\n```\nfor n_clusters in range_n_clusters:\n    # Create a subplot with 1 row and 2 columns\n    fig, (ax1, ax2) = plt.subplots(1, 2)\n    fig.set_size_inches(18, 7)\n\n    # The 1st subplot is the silhouette plot\n    # The silhouette coefficient can range from -1, 1 but in this example all\n    # lie within [-0.1, 1]\n    ax1.set_xlim([-0.1, 1])\n    # The (n_clusters+1)*10 is for inserting blank space between silhouette\n    # plots of individual clusters, to demarcate them clearly.\n    ax1.set_ylim([0, len(X) + (n_clusters + 1) * 10])\n\n    # Initialize the clusterer with n_clusters value and a random generator\n    # seed of 10 for reproducibility.\n    clusterer = KMeans(n_clusters=n_clusters, random_state=10)\n    cluster_labels = clusterer.fit_predict(X)\n\n    # The silhouette_score gives the average value for all the samples.\n    # This gives a perspective into the density and separation of the formed\n    # clusters\n    silhouette_avg = silhouette_score(X, cluster_labels)\n    print(\"For n_clusters =\", n_clusters,\n          \"The average silhouette_score is :\", silhouette_avg)\n\n    # Compute the silhouette scores for each sample\n    sample_silhouette_values = silhouette_samples(X, cluster_labels)\n\n    y_lower = 10\n    for i in range(n_clusters):\n        # Aggregate the silhouette scores for samples belonging to\n        # cluster i, and sort them\n        ith_cluster_silhouette_values = \n            sample_silhouette_values[cluster_labels == i]\n\n        ith_cluster_silhouette_values.sort()\n\n        size_cluster_i = ith_cluster_silhouette_values.shape[0]\n        y_upper = y_lower + size_cluster_i\n\n        color = cm.nipy_spectral(float(i) / n_clusters)\n        ax1.fill_betweenx(np.arange(y_lower, y_upper),\n                          0, ith_cluster_silhouette_values,\n                          facecolor=color, edgecolor=color, alpha=0.7)\n\n        # Label the silhouette plots with their cluster numbers at the middle\n        ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n\n        # Compute the new y_lower for next plot\n        y_lower = y_upper + 10  # 10 for the 0 samples\n\n    ax1.set_title(\"The silhouette plot for the various clusters.\")\n    ax1.set_xlabel(\"The silhouette coefficient values\")\n    ax1.set_ylabel(\"Cluster label\")\n\n    # The vertical line for average silhouette score of all the values\n    ax1.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n\n    ax1.set_yticks([])  # Clear the yaxis labels / ticks\n    ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n\n    # 2nd Plot showing the actual clusters formed\n    colors = cm.nipy_spectral(cluster_labels.astype(float) / n_clusters)\n    ax2.scatter(X[:, 0], X[:, 1], marker='.', s=30, lw=0, alpha=0.7,\n                c=colors, edgecolor='k')\n\n    # Labeling the clusters\n    centers = clusterer.cluster_centers_\n    # Draw white circles at cluster centers\n    ax2.scatter(centers[:, 0], centers[:, 1], marker='o',\n                c=\"white\", alpha=1, s=200, edgecolor='k')\n\n    for i, c in enumerate(centers):\n        ax2.scatter(c[0], c[1], marker='$%d$' % i, alpha=1,\n                    s=50, edgecolor='k')\n\n    ax2.set_title(\"The visualization of the clustered data.\")\n    ax2.set_xlabel(\"Feature space for the 1st feature\")\n    ax2.set_ylabel(\"Feature space for the 2nd feature\")\n\n    plt.suptitle((\"Silhouette analysis for KMeans clustering on sample data \"\n                  \"with n_clusters = %d\" % n_clusters),\n                 fontsize=14, fontweight='bold')\n\nplt.show()\n```\n\n![Code output in the console \\(image created by the author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/09/10i3uGdxPhweTnuJWiZ8LSA.png)\nCode output in the console (image created by the author)\n> We observe that the average/mean **Silhouette Score is the highest in the case of K=4 clusters.**\n**This verifies that the Silhouette Score is a good measure of K-means fitting goodness.**\nWe also produced these Figures:\n![Silhouette score &amp; the data with the final \\(converged\\) centroids \\(image created by the author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/09/1lh-a75nwg8gi_aMGcaQkSA.png)\nSilhouette score & the data with the final (**converged**) **centroids** (image created by the author)\n![Silhouette score &amp; the data with the final \\(converged\\) centroids \\(image created by the author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/09/1WqFNuHkvtk-3g6ilYovuKQ.png)\nSilhouette score & the data with the final (**converged**) **centroids** (image created by the author)\n![Silhouette score &amp; the data with the final \\(converged\\) centroids \\(image created by the author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/09/1cAbr_kZBlRJ8vUb0kvs78Q.png)\nSilhouette score & the data with the final (**converged**) **centroids** (image created by the author)\n**The vertical line is the average silhouette score of all the values.**\n**Again, we can also visually verify that the Silhouette Score is a good measure of K-means fitting goodness for the specific example.**\n* * *\n## **Conclusions**\n**K-means** is one of the most widely used unsupervised clustering methods. The algorithm clusters the data at hand by trying to separate samples into **K** groups of equal variance, minimizing a criterion known as the inertia or within-cluster sum-of-squares. This algorithm requires the number of clusters to be specified.\nHowever, if the ground truth labels are not known, the evaluation must be performed using the model itself.\nIn this article we used the **Silhouette Coefficient.** From the working example, we can verify that the Silhouette Score is a good measure of K-means fitting goodness.\n* * *\nThat's all! Hope you liked this new article - more to come soon!\n* * *\n## References\n[1] Peter J. Rousseeuw (1987). \"Silhouettes: a Graphical Aid to the Interpretation and Validation of Cluster Analysis\". Computational and Applied Mathematics 20: 53–65. [doi:10.1016/0377–0427(87)90125–7](https://doi.org/10.1016/0377-0427\\(87\\)90125-7).\n[2] <https://en.wikipedia.org/wiki/Silhouette_(clustering)>\n* * *\n### You may also like:\n> [**LSTM Time-Series Forecasting: Predicting Stock Prices Using An LSTM Model**](https://towardsdatascience.com/lstm-time-series-forecasting-predicting-stock-prices-using-an-lstm-model-6223e9644a2f)\n> [**Time-Series Forecasting: Predicting Stock Prices Using An ARIMA Model**](https://towardsdatascience.com/time-series-forecasting-predicting-stock-prices-using-an-arima-model-2e3b3080bd70)\n* * *\n## Stay tuned & support this effort\nIf you liked and found this article useful, **follow** me to be able to see all my new posts.\nQuestions? 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