Lecture_7(Basics_of_Kmeans).ipynb

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        "- https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.vq.vq.html\n",
        "- https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.vq.kmeans.html"
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        "**Why k-means clustering?**<br/>\n",
        "We explored a critical issue in hierarchical clustering in the last chapter - runtime."
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        "To perform K-Means clustering in scipy, there are two steps involved - generate the cluster centers and then assign the cluster labels. The first step is performed by the kmeans method. The k-means function returns two arguments, the cluster centers and distortion. The cluster centers, is also known as the code book.\n",
        "- In K-means clustering, distortion refers to the sum of squared distances between each data point and its assigned cluster center. It's also commonly known as the \"within-cluster sum of squares\" (WCSS).\n",
        "\n",
        "The next step is to use the vq method to generate cluster labels.The function returns the cluster labels, also known as the \"code book index\" and the distortion."
      ],
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        "**A note on distortions**<br/>\n",
        "kmeans returns a single value of distortions based on the overall data, whereas vq returns a list of distortions, one for each data point. The mean of the list of distortions from the vq method should approximately equal the distortion value of the kmeans method if the same list of observations is passed."
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      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
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        "comic_con = pd.read_csv('/content/comic_con.csv')\n",
        "comic_con.head()"
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              "   x_coordinate  y_coordinate\n",
              "0            17             4\n",
              "1            20             6\n",
              "2            35             0\n",
              "3            14             0\n",
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        "# normalize data for clustering\n",
        "from scipy.cluster.vq import whiten\n",
        "\n",
        "# Use the whiten() function to standardize the data\n",
        "comic_con['x_scaled'] = whiten(comic_con['x_coordinate'])\n",
        "comic_con['y_scaled'] = whiten(comic_con['y_coordinate'])\n",
        "\n",
        "comic_con.head()"
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              "   x_coordinate  y_coordinate  x_scaled  y_scaled\n",
              "0            17             4  0.509349  0.090010\n",
              "1            20             6  0.599234  0.135015\n",
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              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6aad04ce-99e4-4209-93a2-85f2ade9430d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the kmeans and vq functions\n",
        "from scipy.cluster.vq import kmeans, vq\n",
        "\n",
        "# Generate cluster centers\n",
        "cluster_centers, distortion = kmeans(comic_con[['x_scaled', 'y_scaled']], 2)\n",
        "\n",
        "# Assign cluster labels\n",
        "comic_con['cluster_labels'], distortion_list = vq(comic_con[['x_scaled', 'y_scaled']], cluster_centers)\n",
        "\n",
        "# Plot clusters\n",
        "sns.scatterplot(x='x_scaled', y='y_scaled',\n",
        "                hue='cluster_labels', data = comic_con)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "7xmpg9IdrvWv",
        "outputId": "4a4a0e37-8510-4e9c-c7d2-de717100c321"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}