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    "info": {
        "author": "Claudio Schill",
        "author_email": "claudio.schill@icloud.com",
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        "description": "This is an implementation of the **LOCI** (local correlation integral) fast outlier detection algorithm in Python, based on the paper:  \nPapadimitriou, S., Kitagawa, H., Gibbons, P.B. and Faloutsos, C., 2003, March. *Loci: Fast outlier detection using the local correlation integral*. In Data Engineering, 2003. Proceedings. 19th International Conference on (pp. 315-326). IEEE.\n\nThis is an initial first attempt implementation, it is functional (I think) however performance is very limited.\n\n\n##### Installation\n\n```bash\npip install loci\n```\n\n##### Dependencies\n\n- Scipy, Numpy\n- Python version 3.6 (probably works with 3.x, not tested though)\n\n##### Example\n\n```python\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom loci.loci import run_loci\n\ndata = np.concatenate((np.array([(10, 10), ]), np.random.normal(50, 10, (200, 2))))\n\nloci_res = run_loci(data)\noutlier_indices = loci_res.outlier_indices\n\nplt.scatter(data[:, 0], data[:, 1])\nplt.scatter(data[outlier_indices, 0], data[outlier_indices, 1], c='r')\nplt.show()\n```\n\n\n\n\n",
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        "license": "MIT",
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        "name": "loci",
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        "summary": "Implementation of the LOCI outlier detection algorithm in Python",
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