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Works on nodejs and browser.\r\n\r\n## Installation\r\n```\r\nnpm install skmeans\r\n```\r\n\r\n## Usage\r\n### NodeJS\r\n```javascript\r\nconst skmeans = require(\"skmeans\");\r\n\r\nvar data = [1,12,13,4,25,21,22,3,14,5,11,2,23,24,15];\r\nvar res = skmeans(data,3);\r\n```\r\n\r\n### Browser\r\n```html\r\n<!doctype html>\r\n<html>\r\n<head>\r\n\t<script src=\"skmeans.js\"></script>\r\n</head>\r\n<body>\r\n\t<script>\r\n\t\tvar data = [1,12,13,4,25,21,22,3,14,5,11,2,23,24,15];\r\n\t\tvar res = skmeans(data,3);\r\n\r\n\t\tconsole.log(res);\r\n\t</script>\r\n</body>\r\n</html>\r\n```\r\n\r\n## Results\r\n```javascript\r\n{\r\n\tit: 2,\r\n\tk: 3,\r\n\tidxs: [ 2, 0, 0, 2, 1, 1, 1, 2, 0, 2, 0, 2, 1, 1, 0 ],\r\n\tcentroids: [ 13, 23, 3 ]\r\n}\r\n```\r\n\r\n## API\r\n### skmeans(data,k,[centroids],[iterations])\r\nCalculates unidimiensional and multidimensional k-means clustering on *data*. Parameters are:\r\n* **data** Unidimiensional or multidimensional array of values to be clustered. for unidimiensional data, takes the form of a simple array *[1,2,3.....,n]*. For multidimensional data, takes a\r\nNxM array *[[1,2],[2,3]....[n,m]]*\r\n* **k** Number of clusters\r\n* **centroids** Optional. Initial centroid values. If not provided, the algorith will try to choose an apropiate ones. Alternative values can be:\r\n  * **\"kmrand\"** Cluster initialization will be random, but with extra checking, so there will no be two equal initial centroids.\r\n  * **\"kmpp\"** The algorythm will use the [k-means++](https://en.wikipedia.org/wiki/K-means%2B%2B) cluster initialization method.\r\n* **iterations** Optional. Maximum number of iterations. If not provided, it will be set to 10000.\r\n* **distance function** Optional. Custom distance function. Takes two points as arguments and returns a scalar number.\r\n\r\nThe function will return an object with the following data:\r\n* **it** The number of iterations performed until the algorithm has converged\r\n* **k** The cluster size\r\n* **centroids** The value for each centroid of the cluster\r\n* **idxs** The index to the centroid corresponding to each value of the data array\r\n* **test** Function to test new point membership\r\n\r\n## Examples\r\n```javascript\r\n// k-means with 3 clusters. Random initialization\r\nvar res = skmeans(data,3);\r\n\r\n// k-means with 3 clusters. Initial centroids provided\r\nvar res = skmeans(data,3,[1,5,9]);\r\n\r\n// k-means with 3 clusters. k-means++ cluster initialization\r\nvar res = skmeans(data,3,\"kmpp\");\r\n\r\n// k-means with 3 clusters. Random initialization. 10 max iterations\r\nvar res = skmeans(data,3,null,10);\r\n\r\n// k-means with 3 clusters. Custom distance function\r\nvar res = skmeans(data,3,null,null,(x1,x2)=>Math.abs(x1-x2));\r\n\r\n// Test new point\r\nvar res = skmeans(data,3,null,10);\r\nres.test(6);\r\n\r\n// Test new point with custom distance\r\nvar res = skmeans(data,3,null,10);\r\nres.test(6,(x1,x2)=>Math.abs(x1-x2));\r\n```\r\n","maintainers":[{"name":"solzimer","email":"solzimer@gmail.com"}],"time":{"modified":"2022-06-26T20:36:10.949Z","created":"2017-06-05T15:50:58.465Z","0.2.0":"2017-06-05T15:50:58.465Z","0.3.0":"2017-06-05T22:40:31.015Z","0.3.1":"2017-06-05T22:43:59.859Z","0.4.0":"2017-06-07T22:51:25.212Z","0.5.0":"2017-06-07T23:37:57.005Z","0.5.1":"2017-06-16T23:22:15.592Z","0.5.2":"2017-06-18T00:11:09.219Z","0.6.0":"2017-06-18T21:29:47.695Z","0.6.1":"2017-06-18T21:31:56.734Z","0.7.0":"2017-07-13T22:17:40.998Z","0.8.0":"2017-07-17T00:17:25.953Z","0.8.1":"2017-07-17T10:56:15.603Z","0.8.2":"2017-07-17T11:06:14.554Z","0.8.3":"2017-07-17T11:29:48.469Z","0.8.4":"2017-07-17T17:01:56.130Z","0.8.5":"2017-07-17T17:03:10.232Z","0.9.0":"2017-07-17T22:56:19.462Z","0.9.1":"2017-07-18T12:37:41.952Z","0.9.2":"2017-07-18T23:04:52.852Z","0.9.3":"2017-07-19T22:34:29.492Z","0.9.4":"2017-07-20T22:23:40.475Z","0.9.5":"2017-07-21T12:40:50.911Z","0.9.6":"2017-08-03T09:30:45.386Z","0.9.7":"2017-08-03T09:34:49.073Z","0.9.8":"2018-07-14T14:44:19.278Z","0.10.0":"2019-03-02T23:53:31.392Z","0.10.1":"2019-04-13T13:59:03.932Z","0.10.2":"2019-05-24T16:01:29.655Z","0.11.0":"2019-08-27T10:51:33.516Z","0.11.1":"2019-09-13T18:10:51.968Z","0.11.2":"2019-09-13T18:18:18.536Z","0.11.3":"2020-01-29T10:46:13.833Z"},"homepage":"https://github.com/solzimer/skmeans#readme","keywords":["math","k-means","k-means++","kmeans++","kmeans","simple","cluster","fast","unidimiensional","multidimensional"],"repository":{"type":"git","url":"git+https://github.com/solzimer/skmeans.git"},"author":{"name":"David Gómez Matarrodona","email":"solzimer@gmail.com"},"bugs":{"url":"https://github.com/solzimer/skmeans/issues"},"license":"MIT","readmeFilename":"README.md","users":{"solzimer":true}}