Optimizing K-Means Clustering for Time Series Data - DZone AI

Here at New Relic, we collect 1.37 billion data points per minute. A vast amount of the data we collect, analyze, and display for our customers is stored as time series. In an effort to build relationships between applications and other entities, such as servers and containers, for new, intelligent products like New Relic Radar, we're constantly exploring faster and more efficient methods of grouping time series data. Given the amount of data we collect, faster clustering times are crucial. A popular method of grouping data is k-means clustering.