If we could know in 2020 what we will know in 2025 (only five foreseeable years into the future), how would we change our attitudes, actions, and the way in which we practice law, the services we offer, the clients we target, and the ways in which we choose to deliver our services? Indeed - if we could have known a year ago the events of the first three months in 2020, what might we have done to prepare? The American writer and humorist, Mark Twain, advised: "When everybody is out digging for gold, the business to be in is selling shovels!" So, what foreseeable trend may represent the figurative "shovel" that every client will need tomorrow?

By clustering data into homogeneous groups, analysts can accurately detect anomalies within an image. This research was conducted to determine the most robust algorithm and settings for clustering hyperspectral images. Multiple images were analyzed, employing a variety of clustering algorithms under numerous conditions to include distance measurements for the algorithms and prior data reduction techniques. Various clustering algorithms were employed, including a hierarchical method, ISODATA, K-means, and X-means, and were used on a simple two dimensional dataset in order to discover potential problems with the algorithms. Subsequently, the lessons learned were applied to a subset of a hyperspectral image with known clustering, and the algorithms were scored on how well they performed as the number of outliers was increased. The best algorithm was then used to cluster each of the multiple images using every variable combination tested, and the clusters were input into two global anomaly detectors to determine and validate the most robust algorithm settings.