jhcluster

jHC

Description

Hierarchical clustering (agglomerative) is a clustering algorithm that builds a cluster hierarchy from some given points [1].

[1] http://en.wikipedia.org/wiki/Hierarchical_clustering

Additional Features

Besides n-dimensional data, the algorithm also works on data points given by GPS coordinates.

Usage

1. Import the script.

<script type="text/javascript" src="jHC.js"></script>

1. Sample Data Format

Basic Data

let point_data = [
    [0.1, 5],
    [2, 4],
    [0, 7]
];

GPS Data

The latitude is the first number in the pair followed by the longitude.

let gps_point_data = [
    [55.7858667, 12.5233995],
    [45.4238667, 12.5233995],
    [25.3438667, 11.6533995]
];

1. Run the algorithm. To run the algorithm you need to provide the data along with the linkage and distance parameters. For the traditional hierarchical clustering the steps are the following:

let hc = jHC()
    .linkage('AVERAGE')
    .distance('EUCLIDEAN')
    .data(point_data);

The distance functions available are: 'EUCLIDEAN', 'HAVERSINE' (for GPS data), 'MANHATTAN'. The linkages implemented are 'AVERAGE', 'COMPLETE' and 'SINGLE'. Additionally you can provide your own distance function, which must accept at least two parameters (the two points), and then pass it to the distance method as its parameter. The next step is to simply run the clustering algorithm.

// This will run the aglorithm and return the resulting hierarchy tree.
let hierarchy_tree = hc();

Result Format

The end root node is wrapped in an array and returned. The resulting clustering hierarchy has the following format:

[
    {
        children: [
            // the two children of the root hierarchy node.
            {
                children: [
                    {
                        //leaf node
                        coordinates: [9, 5],
                        name: 1,
                        size: 1
                    },
                    {
                        //leaf node
                        coordinates: [7, 9],
                        name: 2,
                        size: 1
                    }
                ],
                coordinates: [11, 12],
                name: 3,
                size: 2
            },
            {
                //leaf node
                coordinates: [9, 9],
                name: 0,
                size: 1
            }
        ],
        coordinates: [13, 14], // centroid coordinates for the cluster
        name: 4, // id of cluster
        size: 3 // number of points contained
    }
];

Additionally you can get the leaf nodes with the leafNodes function.

Example

See working example in the example/example.html file. Use the console to inspect the input and output of the algorithm.