{"id":24374547,"url":"https://github.com/hansalemaos/cyhdbscan","last_synced_at":"2026-03-05T18:02:46.349Z","repository":{"id":273116624,"uuid":"918756856","full_name":"hansalemaos/cyhdbscan","owner":"hansalemaos","description":"Very fast hdbscan for Python - written in Cython/C++","archived":false,"fork":false,"pushed_at":"2025-01-18T19:18:22.000Z","size":20,"stargazers_count":4,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-01-07T20:20:19.391Z","etag":null,"topics":["cpp","cython","data-science","euclidean","fast","hdbscan","python"],"latest_commit_sha":null,"homepage":"https://pypi.org/project/cyhdbscan","language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/hansalemaos.png","metadata":{"files":{"readme":"README.MD","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-01-18T19:18:20.000Z","updated_at":"2025-09-18T21:19:15.000Z","dependencies_parsed_at":"2025-01-18T19:47:20.583Z","dependency_job_id":null,"html_url":"https://github.com/hansalemaos/cyhdbscan","commit_stats":null,"previous_names":["hansalemaos/cyhdbscan"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/hansalemaos/cyhdbscan","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hansalemaos%2Fcyhdbscan","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hansalemaos%2Fcyhdbscan/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hansalemaos%2Fcyhdbscan/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hansalemaos%2Fcyhdbscan/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/hansalemaos","download_url":"https://codeload.github.com/hansalemaos/cyhdbscan/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hansalemaos%2Fcyhdbscan/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30141296,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-05T16:58:46.102Z","status":"ssl_error","status_checked_at":"2026-03-05T16:58:45.706Z","response_time":93,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cpp","cython","data-science","euclidean","fast","hdbscan","python"],"created_at":"2025-01-19T05:40:38.913Z","updated_at":"2026-03-05T18:02:46.320Z","avatar_url":"https://github.com/hansalemaos.png","language":"C++","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Python Wrapper for HDBSCAN-C++\n\n### `pip install cyhdbscan`\n\nThis repository contains a Python wrapper for the [HDBSCAN-C++ implementation by Rohan Mohapatra / Sumedh Basarkod](https://github.com/rohanmohapatra/hdbscan-cpp) . It allows you to perform HDBSCAN clustering directly from Python using Cython to bridge between Python and C++. It has no dependencies (except Cython for the compilation)\n\n## Features\n\n- Utilize the fast and efficient HDBSCAN algorithm implemented in C++\n- Easy to use from Python\n- Supports different distance metrics - Euclidean and Manhattan\n\n## Prerequisites\n\nBefore you can use this wrapper, ensure you have the following installed:\n- Python (of course)\n- Cython\n- A C++ compiler (e.g., GCC or MSVC)\n\n## Usage example\n\n```py\nfrom cyhdbscan import py_calculate_hdbscan # The lib will be compiled the first time you import it\n\ndataset = [\n    (0.837, 2.136),\n    (-1.758, 2.974),\n    (1.190, 4.728),\n    (2.140, 0.706),\n    (-1.035, 8.206),\n    (1.255, 0.090),\n    (0.596, 4.086),\n    (1.280, 1.058),\n    (1.730, 1.147),\n    (-0.949, 8.464),\n    (0.935, 5.332),\n    (2.369, 0.795),\n    (0.429, 4.974),\n    (-2.048, 6.654),\n    (-1.457, 7.487),\n    (0.529, 3.808),\n    (1.782, 0.908),\n    (-1.956, 8.616),\n    (-1.746, 3.012),\n    (-1.180, 3.128),\n    (1.164, 3.791),\n    (1.362, 1.366),\n    (2.601, 1.088),\n    (0.272, 5.470),\n    (-3.122, 3.282),\n    (-0.588, 8.614),\n    (1.669, -0.436),\n    (-0.683, 7.675),\n    (2.368, 0.552),\n    (1.052, 4.545),\n    (2.227, 1.263),\n    (2.439, -0.073),\n    (1.345, 4.857),\n    (-1.315, 6.839),\n    (0.983, 5.375),\n    (-1.063, 2.208),\n    (-1.607, 3.565),\n    (1.573, 0.484),\n    (-2.179, 8.086),\n    (1.834, 0.754),\n    (2.106, 3.495),\n    (-1.643, 7.527),\n    (1.106, 1.264),\n    (1.612, 1.823),\n    (0.460, 5.450),\n    (-0.538, 3.016),\n    (1.678, 0.609),\n    (-1.012, 3.603),\n    (1.342, 0.594),\n    (1.428, 1.624),\n    (2.045, 1.125),\n    (1.673, 0.659),\n    (-1.359, 2.322),\n    (1.131, 0.936),\n    (-1.739, 1.948),\n    (-0.340, 8.167),\n    (-1.638, 2.433),\n    (-1.688, 2.241),\n    (2.430, -0.064),\n    (-1.380, 7.185),\n    (-1.252, 2.339),\n    (-2.395, 3.398),\n    (-2.092, 7.481),\n    (0.488, 3.268),\n    (-0.539, 7.456),\n    (-2.592, 8.076),\n    (-1.047, 2.965),\n    (1.256, 3.382),\n    (-1.622, 4.272),\n    (1.869, 5.441),\n    (-1.764, 2.222),\n    (-1.382, 7.288),\n    (0.008, 4.176),\n    (-1.103, 7.302),\n    (-1.794, 7.581),\n    (-1.512, 7.944),\n    (0.959, 4.561),\n    (-0.601, 6.300),\n    (0.225, 4.770),\n    (1.567, 0.018),\n    (-1.034, 2.921),\n    (-0.922, 8.099),\n    (-1.886, 2.248),\n    (1.869, 0.956),\n    (1.101, 4.890),\n    (-1.932, 8.306),\n    (0.670, 4.041),\n    (0.744, 4.122),\n    (1.640, 1.819),\n    (0.815, 4.785),\n    (-2.633, 2.631),\n    (-0.961, 1.274),\n    (0.214, 4.885),\n    (1.435, 1.307),\n    (1.214, 3.648),\n    (1.083, 4.063),\n    (-1.226, 8.296),\n    (1.482, 0.690),\n    (1.896, 5.185),\n    (-1.324, 4.131),\n    (-1.150, 7.893),\n    (2.469, 1.679),\n    (2.311, 1.304),\n    (0.573, 4.088),\n    (-0.968, 3.122),\n    (2.625, 0.950),\n    (1.684, 4.196),\n    (-2.221, 2.731),\n    (-1.578, 3.034),\n    (0.082, 4.567),\n    (1.433, 4.377),\n    (1.063, 5.176),\n    (0.768, 4.398),\n    (2.470, 1.315),\n    (-1.732, 7.164),\n    (0.347, 3.452),\n    (-1.001, 2.849),\n    (1.016, 4.485),\n    (0.560, 4.214),\n    (-2.118, 2.035),\n    (-1.362, 2.383),\n    (-2.784, 2.992),\n    (1.652, 3.656),\n    (-1.940, 2.189),\n    (-1.815, 7.978),\n    (1.202, 3.644),\n    (-0.969, 3.267),\n    (1.870, -0.108),\n    (-1.807, 2.068),\n    (1.218, 3.893),\n    (-1.484, 6.008),\n    (-1.564, 2.853),\n    (-0.686, 8.683),\n    (1.076, 4.685),\n    (-0.976, 6.738),\n    (1.380, 4.548),\n    (-1.641, 2.681),\n    (-0.002, 4.581),\n    (1.714, 5.025),\n    (-1.405, 7.726),\n    (-0.708, 2.504),\n    (-0.886, 2.646),\n    (1.984, 0.490),\n    (2.952, -0.344),\n    (0.432, 4.335),\n    (-1.866, 7.625),\n    (2.527, 0.618),\n    (2.041, 0.455),\n    (-2.580, 3.188),\n    (1.620, 0.068),\n    (-2.588, 3.131),\n    (0.444, 3.115),\n    (-0.457, 7.306),\n    (-1.129, 7.805),\n    (2.130, 5.192),\n    (1.004, 4.191),\n    (-1.393, 8.746),\n    (0.728, 3.855),\n    (0.893, 1.011),\n    (-1.108, 2.920),\n    (0.789, 4.337),\n    (1.976, 0.719),\n    (-1.249, 3.085),\n    (-1.078, 8.881),\n    (-1.868, 3.080),\n    (2.768, 1.088),\n    (0.277, 4.844),\n    (3.411, 0.872),\n    (-1.581, 7.553),\n    (-1.530, 7.705),\n    (-1.825, 7.360),\n    (-1.686, 7.953),\n    (-1.651, 3.446),\n    (-1.304, 3.003),\n    (-0.731, 6.242),\n    (2.406, 4.870),\n    (-1.536, 3.014),\n    (1.489, 0.652),\n    (0.514, 4.627),\n    (-1.815, 3.290),\n    (-1.937, 3.914),\n    (-0.615, 3.950),\n    (2.032, 0.197),\n    (2.149, 1.037),\n    (-1.370, 7.770),\n    (0.914, 4.550),\n    (0.334, 4.936),\n    (-2.160, 3.410),\n    (1.367, 0.635),\n    (-0.571, 8.133),\n    (-1.006, 3.084),\n    (1.495, 3.858),\n    (-0.590, 7.695),\n    (0.715, 5.413),\n    (2.114, 1.247),\n    (1.201, 0.602),\n    (-2.546, 3.150),\n    (-1.959, 2.430),\n    (2.338, 3.431),\n    (3.353, 1.700),\n    (1.843, 0.073),\n    (1.320, 1.404),\n    (2.097, 4.847),\n    (-1.243, 8.152),\n    (-1.859, 7.789),\n    (2.747, 1.545),\n    (2.608, 1.089),\n    (1.660, 3.563),\n    (2.352, 0.828),\n    (2.223, 0.839),\n    (3.229, 1.132),\n    (-1.559, 7.248),\n    (-0.647, 3.429),\n    (-1.327, 8.515),\n    (0.917, 3.906),\n    (2.295, -0.766),\n    (1.816, 1.120),\n    (-1.120, 7.110),\n    (-1.655, 8.614),\n    (-1.276, 7.968),\n    (1.974, 1.580),\n    (2.518, 1.392),\n    (0.439, 4.536),\n    (0.369, 7.791),\n    (-1.791, 2.750),\n]\n\nresult = py_calculate_hdbscan(\n    data=dataset, min_points=5, min_cluster_size=5, distance_metric=\"Euclidean\"\n)\nimport pandas as pd\n\nprint(pd.DataFrame(result).to_string())\n\n#        original_data  label  membership_probability  outlier_score  outlier_id\n# 0     [0.837, 2.136]      4                0.000000       0.000000          66\n# 1    [-1.758, 2.974]      7                0.000000       0.000000          29\n# 2      [1.19, 4.728]      6                0.000000       0.000000           6\n# 3      [2.14, 0.706]      4                0.742785       0.000000          80\n# 4    [-1.035, 8.206]      3                0.000000       0.000000         188\n# 5      [1.255, 0.09]      4                0.738651       0.000000          48\n# 6     [0.596, 4.086]      6                0.742785       0.000000          76\n# 7      [1.28, 1.058]      4                0.719853       0.000000         103\n# 8      [1.73, 1.147]      4                0.689899       0.000000          70\n# 9    [-0.949, 8.464]      3                0.742785       0.000000         190\n# 10    [0.935, 5.332]      6                0.738651       0.000000         177\n# 11    [2.369, 0.795]      4                0.416808       0.000000         108\n# 12    [0.429, 4.974]      6                0.719853       0.000000         159\n# 13   [-2.048, 6.654]      3                0.738651       0.000000          51\n# 14   [-1.457, 7.487]      3                0.719853       0.000000          97\n# 15    [0.529, 3.808]      6                0.689899       0.000000          86\n# 16    [1.782, 0.908]      4                0.826320       0.000000          82\n# 17   [-1.956, 8.616]      3                0.689899       0.000000         128\n# 18   [-1.746, 3.012]      7                0.742785       0.000000         186\n# 19    [-1.18, 3.128]      7                0.738651       0.000000         166\n# 20    [1.164, 3.791]      6                0.416808       0.000000         118\n# 21    [1.362, 1.366]      4                0.636566       0.000000          87\n# 22    [2.601, 1.088]      4                0.639942       0.000000         133\n# 23     [0.272, 5.47]      6                0.826320       0.000000         117\n# 24   [-3.122, 3.282]      7                0.719853       0.000000          57\n# 25   [-0.588, 8.614]      3                0.416808       0.000000          18\n# 26   [1.669, -0.436]      4                0.582667       0.000000          19\n# 27   [-0.683, 7.675]      3                0.826320       0.000000         185\n# 28    [2.368, 0.552]      4                0.461632       0.000000          41\n# 29    [1.052, 4.545]      6                0.636566       0.000000         169\n# 30    [2.227, 1.263]      4                0.722914       0.000000         168\n# 31   [2.439, -0.073]      4                0.671035       0.000000          74\n# 32    [1.345, 4.857]      6                0.639942       0.000000         219\n# 33   [-1.315, 6.839]      3                0.636566       0.000000         184\n# 34    [0.983, 5.375]      6                0.582667       0.000000           1\n# 35   [-1.063, 2.208]      7                0.689899       0.000000         176\n# 36   [-1.607, 3.565]      7                0.416808       0.000000         131\n# 37    [1.573, 0.484]      4                0.122696       0.000000          92\n# 38   [-2.179, 8.086]      3                0.639942       0.000000         123\n# 39    [1.834, 0.754]      4                0.737856       0.000000          78\n# 40    [2.106, 3.495]      6                0.461632       0.000000         201\n# 41   [-1.643, 7.527]      3                0.582667       0.000000          21\n# 42    [1.106, 1.264]      4                0.673931       0.000000         148\n# 43    [1.612, 1.823]      4                0.721101       0.000000          49\n# 44      [0.46, 5.45]      6                0.722914       0.000000          12\n# 45   [-0.538, 3.016]      7                0.826320       0.000000         196\n# 46    [1.678, 0.609]      4                0.341140       0.000000          93\n# 47   [-1.012, 3.603]      7                0.636566       0.000000           7\n# 48    [1.342, 0.594]      4                0.760534       0.000000          61\n# 49    [1.428, 1.624]      4                0.760116       0.000000         150\n# 50    [2.045, 1.125]      4                0.689325       0.000000         121\n# 51    [1.673, 0.659]      4                0.685775       0.005590         104\n# 52   [-1.359, 2.322]      7                0.639942       0.023094          14\n# 53    [1.131, 0.936]      4                0.701151       0.031076          42\n# 54   [-1.739, 1.948]      7                0.582667       0.056146          39\n# 55    [-0.34, 8.167]      3                0.461632       0.057855         204\n# 56   [-1.638, 2.433]      7                0.461632       0.062953          75\n# 57   [-1.688, 2.241]      7                0.722914       0.080455           2\n# 58    [2.43, -0.064]      4                0.387009       0.081781         139\n# 59    [-1.38, 7.185]      3                0.722914       0.087602          46\n# 60   [-1.252, 2.339]      7                0.671035       0.094225         173\n# 61   [-2.395, 3.398]      7                0.122696       0.098303         116\n# 62   [-2.092, 7.481]      3                0.671035       0.104613         162\n# 63    [0.488, 3.268]      6                0.671035       0.135718         211\n# 64   [-0.539, 7.456]      3                0.122696       0.140783          37\n# 65   [-2.592, 8.076]      3                0.737856       0.160861          56\n# 66   [-1.047, 2.965]      7                0.737856       0.161913         145\n# 67    [1.256, 3.382]      6                0.122696       0.162461         170\n# 68   [-1.622, 4.272]      0                0.000000       0.180867         194\n# 69    [1.869, 5.441]      6                0.737856       0.180867         102\n# 70   [-1.764, 2.222]      7                0.673931       0.180867          50\n# 71   [-1.382, 7.288]      3                0.673931       0.180867         216\n# 72    [0.008, 4.176]      6                0.673931       0.180867          83\n# 73   [-1.103, 7.302]      3                0.721101       0.183881           4\n# 74   [-1.794, 7.581]      3                0.341140       0.183881         100\n# 75   [-1.512, 7.944]      3                0.760534       0.183881         203\n# 76    [0.959, 4.561]      6                0.721101       0.190656         209\n# 77     [-0.601, 6.3]      3                0.760116       0.190656          30\n# 78     [0.225, 4.77]      6                0.341140       0.190656         183\n# 79    [1.567, 0.018]      4                0.326183       0.196035          71\n# 80   [-1.034, 2.921]      7                0.721101       0.203713          11\n# 81   [-0.922, 8.099]      3                0.689325       0.208430         160\n# 82   [-1.886, 2.248]      7                0.341140       0.208627         224\n# 83    [1.869, 0.956]      4                0.417112       0.208888          16\n# 84     [1.101, 4.89]      6                0.760534       0.212415           3\n# 85   [-1.932, 8.306]      3                0.685775       0.217258         112\n# 86     [0.67, 4.041]      6                0.760116       0.217690         153\n# 87    [0.744, 4.122]      6                0.689325       0.226782         157\n# 88     [1.64, 1.819]      4                0.386631       0.233446         171\n# 89    [0.815, 4.785]      6                0.685775       0.249974          54\n# 90   [-2.633, 2.631]      7                0.760534       0.253104          59\n# 91   [-0.961, 1.274]      0                0.000000       0.255871         161\n# 92    [0.214, 4.885]      6                0.701151       0.256637         129\n# 93    [1.435, 1.307]      4                0.470280       0.256637         125\n# 94    [1.214, 3.648]      6                0.387009       0.256637          94\n# 95    [1.083, 4.063]      6                0.326183       0.256637          20\n# 96   [-1.226, 8.296]      3                0.701151       0.256637         214\n# 97     [1.482, 0.69]      4                0.684759       0.261700          22\n# 98    [1.896, 5.185]      6                0.417112       0.261700         113\n# 99   [-1.324, 4.131]      7                0.760116       0.269267         206\n# 100   [-1.15, 7.893]      3                0.387009       0.275141          95\n# 101   [2.469, 1.679]      4                0.838035       0.280604          15\n# 102   [2.311, 1.304]      4                0.727384       0.283523         164\n# 103   [0.573, 4.088]      6                0.386631       0.286972          84\n# 104  [-0.968, 3.122]      7                0.689325       0.288302         147\n# 105    [2.625, 0.95]      4                0.338441       0.292141         208\n# 106   [1.684, 4.196]      6                0.470280       0.300090          28\n# 107  [-2.221, 2.731]      7                0.685775       0.300860         155\n# 108  [-1.578, 3.034]      7                0.701151       0.306750          96\n# 109   [0.082, 4.567]      6                0.684759       0.310031         144\n# 110   [1.433, 4.377]      6                0.838035       0.324773          89\n# 111   [1.063, 5.176]      6                0.727384       0.325524         126\n# 112   [0.768, 4.398]      6                0.338441       0.327308         217\n# 113    [2.47, 1.315]      4                0.635927       0.333191         136\n# 114  [-1.732, 7.164]      3                0.326183       0.335403         221\n# 115   [0.347, 3.452]      6                0.635927       0.336760          52\n# 116  [-1.001, 2.849]      7                0.387009       0.342861         195\n# 117   [1.016, 4.485]      6                0.482353       0.344652         197\n# 118    [0.56, 4.214]      6                0.430840       0.344827         179\n# 119  [-2.118, 2.035]      7                0.326183       0.348280         142\n# 120  [-1.362, 2.383]      7                0.417112       0.352888         105\n# 121  [-2.784, 2.992]      7                0.386631       0.355081         124\n# 122   [1.652, 3.656]      6                0.472956       0.355697          32\n# 123   [-1.94, 2.189]      7                0.470280       0.362003         178\n# 124  [-1.815, 7.978]      3                0.417112       0.363128          81\n# 125   [1.202, 3.644]      6                0.393618       0.372836         135\n# 126  [-0.969, 3.267]      7                0.684759       0.386366           9\n# 127   [1.87, -0.108]      4                0.482353       0.387209           8\n# 128  [-1.807, 2.068]      7                0.838035       0.391382         191\n# 129   [1.218, 3.893]      6                0.743208       0.392763         120\n# 130  [-1.484, 6.008]      3                0.386631       0.395156         114\n# 131  [-1.564, 2.853]      7                0.727384       0.397227         213\n# 132  [-0.686, 8.683]      3                0.470280       0.401954         222\n# 133   [1.076, 4.685]      6                0.502175       0.402517         109\n# 134  [-0.976, 6.738]      3                0.684759       0.403438         141\n# 135    [1.38, 4.548]      6                0.766235       0.418471          62\n# 136  [-1.641, 2.681]      7                0.338441       0.432980          53\n# 137  [-0.002, 4.581]      6                0.189619       0.437722          73\n# 138   [1.714, 5.025]      6                0.759588       0.439706         200\n# 139  [-1.405, 7.726]      3                0.838035       0.439706          79\n# 140  [-0.708, 2.504]      7                0.635927       0.439706         127\n# 141  [-0.886, 2.646]      7                0.482353       0.439706         182\n# 142    [1.984, 0.49]      4                0.430840       0.441015         146\n# 143  [2.952, -0.344]      4                0.472956       0.457827          85\n# 144   [0.432, 4.335]      6                0.624909       0.457827         218\n# 145  [-1.866, 7.625]      3                0.727384       0.458990         119\n# 146   [2.527, 0.618]      4                0.393618       0.463463         137\n# 147   [2.041, 0.455]      4                0.743208       0.470460          60\n# 148   [-2.58, 3.188]      7                0.430840       0.470825         149\n# 149    [1.62, 0.068]      4                0.502175       0.483465         165\n# 150  [-2.588, 3.131]      7                0.472956       0.485582          38\n# 151   [0.444, 3.115]      0                0.000000       0.492651         163\n# 152  [-0.457, 7.306]      3                0.338441       0.505280          33\n# 153  [-1.129, 7.805]      3                0.635927       0.505572         172\n# 154    [2.13, 5.192]      6                0.417656       0.511402         111\n# 155   [1.004, 4.191]      6                0.441913       0.511402         193\n# 156  [-1.393, 8.746]      3                0.482353       0.516557         192\n# 157   [0.728, 3.855]      6                0.438766       0.516557          27\n# 158   [0.893, 1.011]      4                0.766235       0.518120         110\n# 159   [-1.108, 2.92]      7                0.393618       0.521854         189\n# 160   [0.789, 4.337]      6                0.758176       0.524485         101\n# 161   [1.976, 0.719]      4                0.189619       0.526365         212\n# 162  [-1.249, 3.085]      7                0.743208       0.532096         156\n# 163  [-1.078, 8.881]      3                0.430840       0.535303          67\n# 164   [-1.868, 3.08]      7                0.502175       0.536606          98\n# 165   [2.768, 1.088]      4                0.759588       0.536606         202\n# 166   [0.277, 4.844]      6                0.425497       0.536606         154\n# 167   [3.411, 0.872]      4                0.624909       0.536606         138\n# 168  [-1.581, 7.553]      3                0.472956       0.543011         122\n# 169   [-1.53, 7.705]      3                0.393618       0.544018         207\n# 170   [-1.825, 7.36]      3                0.743208       0.544851          35\n# 171  [-1.686, 7.953]      3                0.502175       0.547966         187\n# 172  [-1.651, 3.446]      7                0.766235       0.555784         220\n# 173  [-1.304, 3.003]      7                0.189619       0.560654          25\n# 174  [-0.731, 6.242]      3                0.766235       0.564289          10\n# 175    [2.406, 4.87]      6                0.806437       0.572578          45\n# 176  [-1.536, 3.014]      7                0.759588       0.576194         107\n# 177   [1.489, 0.652]      4                0.417656       0.577034         205\n# 178   [0.514, 4.627]      6                0.671555       0.579684          44\n# 179   [-1.815, 3.29]      7                0.624909       0.582450          47\n# 180  [-1.937, 3.914]      7                0.417656       0.587861          34\n# 181   [-0.615, 3.95]      0                0.000000       0.600229          90\n# 182   [2.032, 0.197]      4                0.441913       0.600301         132\n# 183   [2.149, 1.037]      4                0.438766       0.602544         140\n# 184    [-1.37, 7.77]      3                0.189619       0.604401          36\n# 185    [0.914, 4.55]      6                0.738818       0.610496         134\n# 186   [0.334, 4.936]      6                0.779360       0.611313          17\n# 187    [-2.16, 3.41]      7                0.441913       0.615856          64\n# 188   [1.367, 0.635]      4                0.758176       0.616716          55\n# 189  [-0.571, 8.133]      3                0.759588       0.617330          23\n# 190  [-1.006, 3.084]      7                0.438766       0.618283         180\n# 191   [1.495, 3.858]      6                0.638316       0.619707         106\n# 192   [-0.59, 7.695]      3                0.624909       0.621117          43\n# 193   [0.715, 5.413]      6                0.610878       0.621668           5\n# 194   [2.114, 1.247]      4                0.425497       0.622072         158\n# 195   [1.201, 0.602]      4                0.806437       0.628202          72\n# 196   [-2.546, 3.15]      7                0.758176       0.629062         115\n# 197   [-1.959, 2.43]      7                0.425497       0.630759          88\n# 198   [2.338, 3.431]      0                0.000000       0.640282          26\n# 199     [3.353, 1.7]      4                0.671555       0.643993          24\n# 200   [1.843, 0.073]      4                0.738818       0.652346         152\n# 201    [1.32, 1.404]      4                0.779360       0.666485          31\n# 202   [2.097, 4.847]      6                0.642165       0.673339          58\n# 203  [-1.243, 8.152]      3                0.417656       0.675609          63\n# 204  [-1.859, 7.789]      3                0.441913       0.676482          99\n# 205   [2.747, 1.545]      4                0.638316       0.676673          69\n# 206   [2.608, 1.089]      4                0.610878       0.689151         210\n# 207    [1.66, 3.563]      6                0.331853       0.708094          13\n# 208   [2.352, 0.828]      4                0.642165       0.709907         175\n# 209   [2.223, 0.839]      4                0.331853       0.710540          40\n# 210   [3.229, 1.132]      4                0.680165       0.713226          65\n# 211  [-1.559, 7.248]      3                0.438766       0.718864         181\n# 212  [-0.647, 3.429]      7                0.806437       0.731078         174\n# 213  [-1.327, 8.515]      3                0.758176       0.740460         151\n# 214   [0.917, 3.906]      6                0.680165       0.747472         130\n# 215  [2.295, -0.766]      4                0.760572       0.751100          68\n# 216    [1.816, 1.12]      4                0.324560       0.752195         215\n# 217    [-1.12, 7.11]      3                0.425497       0.758514          77\n# 218  [-1.655, 8.614]      3                0.806437       0.766876         167\n# 219  [-1.276, 7.968]      3                0.671555       0.767946         223\n# 220    [1.974, 1.58]      4                0.657128       0.771445         199\n# 221   [2.518, 1.392]      4                0.546996       0.779360           0\n# 222   [0.439, 4.536]      6                0.760572       0.782303         198\n# 223   [0.369, 7.791]      3                0.738818       0.816012         143\n# 224   [-1.791, 2.75]      7                0.671555       0.827391          91\n```","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhansalemaos%2Fcyhdbscan","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhansalemaos%2Fcyhdbscan","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhansalemaos%2Fcyhdbscan/lists"}