ODMeansSampleData

A synthetic data frame containing 1700 Origin-Destination points.

datasets

OD-means is a hierarchical adaptive k-means algorithm based on origin-destination pairs.
In the first layer of the hierarchy, the clusters are separated automatically based on the variation
of the within-cluster distance of each cluster until convergence. The second layer of the hierarchy
corresponds to the sub clustering process of small clusters based on the distance between the origin
and destination of each cluster.

Sebastian Moreno

ODMeans

OD-Means: k-Means for Origin-Destination

Cristobal Heredia

Wilfredo Yushimito

Gonzalo Huerta

ODMeansSampleData function

A data frame with 10000 rows and 5 variables:<dl>
 <dt>OriginLatitude</dt>
<dd>Consists of the origin latitude dimension</dd> <dt>OriginLongitude</dt>
<dd>Consists of the origin longitude dimension</dd> <dt>DestinationLatitude</dt>
<dd>Consists of the destination latitude dimension</dd> <dt>DestinationLongitude</dt>
<dd>Consists of the destination longitude dimension</dd> <dt>original_cluster</dt>
<dd>Original cluster of the points when it was created</dd> ...</dl>

Format

Origin-Destination points — ODMeansSampleData

A data frame with 10000 rows and 5 variables:<dl>
 <dt>OriginLatitude</dt>
<dd>Consists of the origin latitude dimension</dd>

 <dt>OriginLongitude</dt>
<dd>Consists of the origin longitude dimension</dd>

 <dt>DestinationLatitude</dt>
<dd>Consists of the destination latitude dimension</dd>

 <dt>DestinationLongitude</dt>
<dd>Consists of the destination longitude dimension</dd>

 <dt>original_cluster</dt>
<dd>Original cluster of the points when it was created</dd>

 ...

</dl>

ODMeansSampleData: Origin-Destination points

Description

Usage

Arguments

Format