K-means – – Finding Anomalies while Clustering
On November 4th and 5th, BigML joined the Qatar Computing Research Institute (QCRI), part of Hamad Bin Khalifa University, to bring
Category: Clusters
How to Discover What is Important to Your Clusters
Raw information is useless if you don’t understand what it means, but sometimes there is just so much it’s hard
Automatically Estimate the Best K for K-Means Clustering with WhizzML
(Thanks to Alex Schwarm of dnb.com for bringing to our attention the Pham, Dimov, and Nguyen paper, which is the subject of
Divining the ‘K’ in K-means Clustering
The venerable K-means algorithm is the a well-known and popular approach to clustering. It does, of course, have some drawbacks.
Visualize Your Data with Dynamic Scatterplots
We have recently announced our Dynamic Scatterplot capability, which is one of many goodies to have come out of BigML
BigML Late Summer Release: Anomaly Detection and More!
Before your sunburns subside and the leaves begin to turn from green to brown, the BigML team is excited to
BigML Clusters in Action!
After a few weeks in Beta, we’ve now released BigML Clusters in final form into the BigML interface and also
BigML Spring Release: A Cluster of Powerful New Features!
The BigML team has been hard at work the past few months. While we haven’t figured out how to work
