1
Introduction
2
Understanding Clusters
▶
2.1
K-means
2.2
G-means
2.3
On the repeatability of Clusters
2.4
Clusters with Images
3
Creating Clusters with 1-Click
4
Cluster Configuration Options
▶
4.1
Clustering algorithms
4.2
Number of clusters
4.3
Critical value
4.4
Default numeric value
4.5
Model clusters
4.6
Scale fields & auto-scaled fields
4.7
Weights
4.8
Sampling Options
4.9
Creating Clusters with Configured Options
4.10
API Request Preview
5
Visualizing Clusters
▶
5.1
Cluster Visualization
5.2
Creating a Model from a Cluster
5.3
Creating a Dataset from a Cluster
5.4
Histograms and Planets
6
Cluster Summary Report
▶
6.1
Cluster Summary
7
Cluster Predictions: Centroids
▼
7.1
Introduction
7.2
Predicting Centroids
7.3
Configuring Centroid Predictions
7.4
Visualizing Cluster Predictions
7.5
Consuming BigML Centroids Programmatically
7.6
Descriptive Information
7.7
Cluster Predictions Privacy
7.8
Moving Centroids
7.9
Stopping Centroids
7.10
Deleting Centroids
8
Consuming Clusters
▶
8.1
Exporting and Downloading Clusters
8.2
Updating Clusters
8.3
Using Clusters via the BigML API
8.4
Using Clusters via the BigML Bindings
9
Clusters Limits
10
Cluster Descriptive Information
▶
10.1
Cluster name
10.2
Description
10.3
Category
10.4
Tags
10.5
Counters
11
Clusters Privacy
12
Moving Clusters to another Project
13
Stopping Cluster Creation
14
Deleting Clusters
15
Takeaways
Bibliography
Glossary
Cluster Analysis with the BigML Dashboard
7 Cluster Predictions: Centroids
7.1
Introduction
7.2
Predicting Centroids
7.3
Configuring Centroid Predictions
7.4
Visualizing Cluster Predictions
7.5
Consuming BigML Centroids Programmatically
7.6
Descriptive Information
7.7
Cluster Predictions Privacy
7.8
Moving Centroids
7.9
Stopping Centroids
7.10
Deleting Centroids
Cluster Summary
Introduction
