00-Overview.ipynb

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    "Clsutering Analysis\n",
    "=====================\n",
    "\n",
    "### Building Models with Distance Metrics\n",
    "\n",
    "This chapter will cover the following topics:\n",
    "* Using KMeans to cluster data\n",
    "* Optimizing the number of centroids\n",
    "* Assessing cluster correctness\n",
    "* Using MiniBatch KMeans to handle more data\n",
    "* Quantizing an image with KMeans clustering\n",
    "* Finding the closest objects in the feature space\n",
    "* Probabilistic clustering with Gaussian Mixture Models\n",
    "* Using KMeans for outlier detection\n",
    "* Using k-NN for regression\n",
    "\n",
    "### Introduction\n",
    "* Clustering is often grouped together with unsupervised techniques.\n",
    "These techniques assume that we do not know the outcome variable.\n",
    "\n",
    "* This leads to ambiguity in outcomes and objectives in practice, but\n",
    "nevertheless, clustering can be useful. As we'll see, we can use clus-\n",
    "tering to \"localize\" our estimates in a supervised setting. This is\n",
    "perhaps why clustering is so effective; it can handle a wide range of\n",
    "situations, and often, the results are for the lack of a better term,\n",
    "\"sane\". We'll walk through a wide variety of applications in this\n",
    "chapter; from image processing to regression and outlier detection.\n",
    "\n",
    "* Through these applications, we'll see that clustering can often be\n",
    "viewed through a probabilistic or optimization lens. Di\u000b",
    "erent inter-\n",
    "pretations lead to various trade-o\u000b",
    "s. We'll walk through how to \f",
    "t\n",
    "the models here so that you have the tools to try out many models\n",
    "when faced with a clustering problem.\n"
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