{
  "id": "2011.06043",
  "version": "v1",
  "published": "2020-11-11T19:54:38.000Z",
  "updated": "2020-11-11T19:54:38.000Z",
  "title": "Clustering of Big Data with Mixed Features",
  "authors": [
    "Joshua Tobin",
    "Mimi Zhang"
  ],
  "comment": "22 pages, 9 figures, for associated Python library, see https://pypi.org/project/CPFcluster/ , submitted to SDM 2021",
  "categories": [
    "stat.ML",
    "cs.LG"
  ],
  "abstract": "Clustering large, mixed data is a central problem in data mining. Many approaches adopt the idea of k-means, and hence are sensitive to initialisation, detect only spherical clusters, and require a priori the unknown number of clusters. We here develop a new clustering algorithm for large data of mixed type, aiming at improving the applicability and efficiency of the peak-finding technique. The improvements are threefold: (1) the new algorithm is applicable to mixed data; (2) the algorithm is capable of detecting outliers and clusters of relatively lower density values; (3) the algorithm is competent at deciding the correct number of clusters. The computational complexity of the algorithm is greatly reduced by applying a fast k-nearest neighbors method and by scaling down to component sets. We present experimental results to verify that our algorithm works well in practice. Keywords: Clustering; Big Data; Mixed Attribute; Density Peaks; Nearest-Neighbor Graph; Conductance.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-11-11T19:54:38.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "big data",
      "mixed features",
      "clustering",
      "fast k-nearest neighbors method",
      "relatively lower density values"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 22,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}