{
  "id": "2007.13185",
  "version": "v1",
  "published": "2020-07-26T17:31:44.000Z",
  "updated": "2020-07-26T17:31:44.000Z",
  "title": "Dimensionality Reduction for $k$-means Clustering",
  "authors": [
    "Neophytos Charalambides"
  ],
  "comment": "20 pages, 1 table, expository",
  "categories": [
    "cs.LG",
    "stat.ML"
  ],
  "abstract": "We present a study on how to effectively reduce the dimensions of the $k$-means clustering problem, so that provably accurate approximations are obtained. Four algorithms are presented, two \\textit{feature selection} and two \\textit{feature extraction} based algorithms, all of which are randomized.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-07-26T17:31:44.000Z"
    }
  ],
  "analyses": {
    "subjects": [
      "65D99",
      "F.2.1"
    ],
    "keywords": [
      "dimensionality reduction",
      "algorithms",
      "provably accurate approximations",
      "means clustering problem",
      "dimensions"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 20,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}