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ml-hclust - npm Package Compare versions
Comparing version 0.0.0 to 0.1.0
| { | ||
| "name": "ml-hclust", | ||
| "version": "0.0.0", | ||
| "version": "0.1.0", | ||
| "description": "Hierarchical clustering algorithms in Javascript", | ||
@@ -5,0 +5,0 @@ "main": "src/index.js", |
@@ -17,91 +17,71 @@ # hclust | ||
| * [AGNES](http://dx.doi.org/10.1002/9780470316801.ch5) (AGglomerative NESting): Continuously merge nodes that have the least dissimilarity. | ||
| * [DIANA](http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0470276800.html) (Divisive ANAlysis): The process starts at the root with all the points as one cluster and recursively splits the higher level clusters to build the dendrogram. | ||
| * [BIRCH](http://www.cs.sfu.ca/CourseCentral/459/han/papers/zhang96.pdf) (Balanced Iterative Reducing and Clustering using Hierarchies): Incrementally construct a CF (Clustering Feature) tree, a hierarchical data structure for multiphase clustering | ||
| * [CURE](http://www.cs.bu.edu/fac/gkollios/ada05/LectNotes/guha98cure.pdf) (Clustering Using REpresentatives): | ||
| * [CHAMELEON](http://www.google.ch/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0CCQQFjAAahUKEwj6t4n_sZbGAhXDaxQKHXCLCmQ&url=http%3A%2F%2Fglaros.dtc.umn.edu%2Fgkhome%2Ffetch%2Fpapers%2FchameleonCOMPUTER99.pdf&ei=kDqBVfqvKsPXUfCWqqAG&usg=AFQjCNEYcGqCxN5N_GlP4Z__UF09aHegQg&sig2=9JkxZ5VS7iDbiJT-imX5Pg&bvm=bv.96041959,d.d24&cad=rja) | ||
| ### new agnes(data,[options]) | ||
| ### new Hclust([options]) | ||
| [AGNES](http://dx.doi.org/10.1002/9780470316801.ch5) (AGglomerative NESting): Continuously merge nodes that have the least dissimilarity. | ||
| Creates a new Hclust instance with the given parameters or the default ones. | ||
| __Arguments__ | ||
| * `options` - Object with options for the algorithm | ||
| __Options__ | ||
| * `data`: Array of points to be clustered, are an array of arrays, as [[x1,y1],[x2,y2], ... ] | ||
| * `options`: Is an object with the parameters `sim` and `kind`, where `sim` is a distance function between vectors (the default function is the euclidean), and `kind` is the string name for the function to calculate distance between clusters, and it could be `single`(default), `complete`, `average`, `centroid` or `ward` | ||
| * `name`: The name of the hierarchical cluster, it could be `agnes`(default), `diana`, `birch`, `cure` or `chameleon`. | ||
| * `sim`: The kind of distance or similarity to use between vectors, the default is `euclidean`, but for a complete list see [ml-distance](https://github.com/mljs/distance). | ||
| * `kind`: The kind of similarity to use between clusters, it could be `single`(default), `complete`, `average`, `centroid` or `ward` | ||
| #### getDendogram([input]) | ||
| Returns a phylogram (a dendogram with weights) and change the leaves values for the values in `input`, if it's given. | ||
| __Example__ | ||
| ```js | ||
| var HCL = require('ml-hclust'); | ||
| var hclust = require('ml-hclust') | ||
| var data = [[2,6], [3,4], [3,8]]; | ||
| var HC = new hclust.agnes(data); | ||
| var dend1 = HC.getDendogram(); | ||
| var dend2 = HC.getDendogram([{a:1},{b:2},{c:3}]); | ||
| ``` | ||
| // actually this are the default values | ||
| var options = { | ||
| name: 'agnes', | ||
| sim: 'euclidean', | ||
| kind: 'single' | ||
| }; | ||
| #### nClusters(N) | ||
| var agnes = new HCL(options); | ||
| ``` | ||
| Returns at least N clusters based in the clustering tree if it's possible | ||
| ### cluster(data) | ||
| ### new diana(data,[options]) | ||
| Creates the tree based in the `data` points. | ||
| [DIANA](http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0470276800.html) (Divisive ANAlysis): The process starts at the root with all the points as one cluster and recursively splits the higher level clusters to build the dendrogram. | ||
| __Arguments__ | ||
| * `data`: Array of points to be clustered. | ||
| * `data`: Array of points to be clustered, are an array of arrays, as [[x1,y1],[x2,y2], ... ] | ||
| * `options`: Is an object with the parameters `sim` and `kind`, where `sim` is a distance function between vectors (the default function is the euclidean), and `kind` is the string name for the function to calculate distance between clusters, and it could be `single`(default), `complete`, `average`, `centroid` or `ward` | ||
| #### getDendogram([input]) | ||
| Returns a phylogram (a dendogram with weights) and change the leaves values for the values in `input`, if it's given. | ||
| __Example__ | ||
| ```js | ||
| var hclust = require('ml-hclust') | ||
| var data = [[2,6], [3,4], [3,8]]; | ||
| var myHcl = new HCL(); | ||
| myHcl.cluster(data); | ||
| var HC = new hclust.diana(data); | ||
| var dend1 = HC.getDendogram(); | ||
| var dend2 = HC.getDendogram([{a:1},{b:2},{c:3}]); | ||
| ``` | ||
| ### getLevel(L) | ||
| #### nClusters(N) | ||
| Returns `L` level of the hierarchical clustering tree. | ||
| Returns at least N clusters based in the clustering tree if it's possible | ||
| __Arguments__ | ||
| ### new birch(data,[options]) | ||
| * `L` - Level of the hierarchical tree. | ||
| [BIRCH](http://www.cs.sfu.ca/CourseCentral/459/han/papers/zhang96.pdf) (Balanced Iterative Reducing and Clustering using Hierarchies): Incrementally construct a CF (Clustering Feature) tree, a hierarchical data structure for multiphase clustering | ||
| __Example__ | ||
| ```js | ||
| // the clustering points | ||
| var agnesData = [[2,6], [3,4], [3,8], [4,5], [4,7], [6,2], [7,2], [7,4], [8,4], [8,5]]; | ||
| ### new cure(data,[options]) | ||
| // creates the hierarchical tree | ||
| var HC = new Hclust(); | ||
| HC.cluster(agnesData); | ||
| [CURE](http://www.cs.bu.edu/fac/gkollios/ada05/LectNotes/guha98cure.pdf) (Clustering Using REpresentatives): | ||
| // the array of clusters | ||
| var ansAgnes = HC.getLevel(3); | ||
| /* | ||
| [ [ [ 3, 4 ], [ 4, 5 ], [ 3, 8 ], [ 4, 7 ] ], | ||
| [ [ 6, 2 ], [ 7, 2 ] ], | ||
| [ [ 7, 4 ], [ 8, 4 ], [ 8, 5 ] ], | ||
| [ [ 2, 6 ] ] ] | ||
| */ | ||
| ``` | ||
| ### export() | ||
| ### new chameleon(data,[options]) | ||
| Exports the model to a JSON object that can be written to disk and reloaded | ||
| [CHAMELEON](http://www.google.ch/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0CCQQFjAAahUKEwj6t4n_sZbGAhXDaxQKHXCLCmQ&url=http%3A%2F%2Fglaros.dtc.umn.edu%2Fgkhome%2Ffetch%2Fpapers%2FchameleonCOMPUTER99.pdf&ei=kDqBVfqvKsPXUfCWqqAG&usg=AFQjCNEYcGqCxN5N_GlP4Z__UF09aHegQg&sig2=9JkxZ5VS7iDbiJT-imX5Pg&bvm=bv.96041959,d.d24&cad=rja) | ||
| ### load(model) | ||
| Returns a new Hclust instance based on the `model`. | ||
| __Arguments__ | ||
| * `model` - JSON object generated with `Hclust.export()` | ||
| ## Test | ||
@@ -108,0 +88,0 @@ |
@@ -141,3 +141,3 @@ 'use strict'; | ||
| * @param {Array <Array <number>>} data - Array of points to be clustered | ||
| * @param options | ||
| * @param {json} options | ||
| * @constructor | ||
@@ -258,2 +258,7 @@ */ | ||
| /** | ||
| * Returns a phylogram and change the leaves values for the values in input | ||
| * @param {Array <object>} input | ||
| * @returns {json} | ||
| */ | ||
| Agnes.prototype.getDendogram = function (input) { | ||
@@ -284,2 +289,7 @@ input = input || {length:this.len, ND: true}; | ||
| /** | ||
| * Returns at least N clusters based in the clustering tree | ||
| * @param {number} N - number of clusters desired | ||
| * @returns {Array <Array <number>>} | ||
| */ | ||
| Agnes.prototype.nClusters = function (N) { | ||
@@ -286,0 +296,0 @@ if (N >= this.len) |
@@ -156,3 +156,3 @@ 'use strict'; | ||
| * @param {Array <Array <number>>} data - Array of points to be clustered | ||
| * @param options | ||
| * @param {json} options | ||
| * @constructor | ||
@@ -272,2 +272,7 @@ */ | ||
| /** | ||
| * Returns a phylogram and change the leaves values for the values in input | ||
| * @param {Array <object>} input | ||
| * @returns {json} | ||
| */ | ||
| Diana.prototype.getDendogram = function (input) { | ||
@@ -298,2 +303,7 @@ input = input || {length:this.len, ND: true}; | ||
| /** | ||
| * Returns at least N clusters based in the clustering tree | ||
| * @param {number} N - number of clusters desired | ||
| * @returns {Array <Array <number>>} | ||
| */ | ||
| Diana.prototype.nClusters = function (N) { | ||
@@ -300,0 +310,0 @@ if (N >= this.len) |
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