ml-hclust - npm Package Compare versions

Comparing version 1.1.1 to 1.1.2

History.md

@@ -0,1 +1,6 @@
+<a name="1.1.2"></a>
+## [1.1.2](https://github.com/mljs/hclust/compare/v1.1.1...v1.1.2) (2016-09-01)
+
+
+
 <a name="1.1.1"></a>
@@ -2,0 +7,0 @@ ## [1.1.1](https://github.com/mljs/hclust/compare/v1.1.0...v1.1.1) (2016-09-01)

package.json

 {
   "name": "ml-hclust",
-  "version": "1.1.1",
+  "version": "1.1.2",
   "description": "Hierarchical clustering algorithms in Javascript",
@@ -5,0 +5,0 @@ "main": "src/index.js",

README.md

@@ -14,82 +14,14 @@ # hclust
 
+## [API Documentation](https://mljs.github.io/hclust/)
+
 ## Methods
 Generate a clustering hierarchy.
 
-### new agnes(data,[options])
+ - [x] [AGNES](http://dx.doi.org/10.1002/9780470316801.ch5) (AGglomerative NESting): Continuously merge nodes that have the least dissimilarity.
+ - [x] [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) 
 
-[AGNES](http://dx.doi.org/10.1002/9780470316801.ch5) (AGglomerative NESting): Continuously merge nodes that have the least dissimilarity.
 
-__Arguments__
-
-* `data`: Array of points to be clustered, are an array of arrays, as [[x1,y1],[x2,y2], ... ]. Optionally the data input can be a distance matrix. In such case, the option `isDistanceMatrix` has to be set to true (by default false).
-* `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 1__
-
-```js
-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}]);
-```
-__Example 2__
-
-```js
-var hclust = require('ml-hclust')
-//A distance matrix. 
-var distance = [[0, 1, 2], [1, 0, 2], [2, 2, 0]]; 
-var HC = new hclust.agnes(data, {source:'distance'});
-```
-
-#### nClusters(N)
-
-Returns at least N clusters based in the clustering tree if it's possible
-
-### new diana(data,[options])
-
-[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, 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 HC = new hclust.diana(data);
-var dend1 = HC.getDendogram();
-var dend2 = HC.getDendogram([{a:1},{b:2},{c:3}]);
-```
-
-#### nClusters(N)
-
-Returns at least N clusters based in the clustering tree if it's possible
-
-### new birch(data,[options])
-
-[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
-
-
-### new cure(data,[options])
-
-[CURE](http://www.cs.bu.edu/fac/gkollios/ada05/LectNotes/guha98cure.pdf) (Clustering Using REpresentatives):
-
-
-### new chameleon(data,[options])
-
-[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) 
-
-
 ## Test
@@ -96,0 +28,0 @@

New alerts

Mixed license

License

(Experimental) Package contains multiple licenses.

Found 1 instance in 1 package

Non-permissive License

License

(Experimental) A license not known to be considered permissive was found.

Found 1 instance in 1 package

Improved metrics

Total package byte prevSize

1588016

5573.31%

Number of package files

35

191.67%

Lines of code

1561

150.56%

Worsened metrics

Number of low license alerts

2

Infinity%

License quality

90

-10%

Number of lines in readme file

49

-58.12%

No dependency changes