1. A system for classifying and labeling data, comprising one or more computer processors configured to: distribute a first random subset of training data to a first host in a distributed system, wherein the first host is configured to independently generate one or more first host clusters from the first random subset of training data; distribute a second random subset of training data to a second host in the distributed system, wherein the second host is configured to independently generate one or more second host clusters from the second random subset of training data; label each of the first and second host clusters with a label that indicates whether the data within the host clusters includes malware, wherein at least one host cluster is assigned a label that indicates inclusion of malware; receive new data; compare the new data to the first and second host clusters; associate the new data with a plurality of host clusters, wherein the plurality of host clusters includes the at least one host cluster assigned the label that indicates inclusion of malware; find that the new data has a maximum associative factor with the at least one host cluster that includes malware; and assign the new data the label that indicates inclusion of malware.