A method for filtering a population of electronic messages transmitted between network nodes and stored in association with respective user accounts on one or more network data storage systems (122, 124, 126) managed by one or more messaging servers, each electronic message being associated with a respective sender, a respective header, and a respective body, the method comprising, by a network node fetching, from one or more of the messaging servers, headers in the population that are stored on one or more of the network data storage systems (122, 124, 126) across multiple of the user accounts; for each of one or more senders, grouping the fetched headers that are associated with the sender into clusters based on a density based clustering process that correlates the clusters with respective dense regions in a clustering data space in which the fetched headers are separated from one another based on similarities between respective pairs of the fetched headers; for each of one or more of the clusters, by network node retrieving, from one or more of the messaging servers, a respective sample of the electronic messages in the population that are associated with the fetched headers in the cluster and stored on one or more of the network data storage systems (122, 124, 126); classifying, with a machine learning classifier, each electronic message in the retrieved sample with a respective label from a predefined set of labels comprising one or more purchase related labels and an associated confidence level to create a respective classification data set for the cluster; assigning to the cluster a respective cluster label selected from the predefined set of labels based on at least one cluster classification rule that maps the respective classification data set to the respective cluster label; for each of one or more clusters assigned a respective one of the purchase related labels, automatically generating a respective filter for filtering purchase related electronic messages, and installing, by a processor, one or more of the filters in at least one network communication channel to select purchase related electronic messages from a set of electronic messages stored in association with respective user accounts on one or more network data storage systems (122, 124, 126) managed by one or more messaging servers. by a network node fetching, from one or more of the messaging servers, headers in the population that are stored on one or more of the network data storage systems (122, 124, 126) across multiple of the user accounts; for each of one or more senders, grouping the fetched headers that are associated with the sender into clusters based on a density based clustering process that correlates the clusters with respective dense regions in a clustering data space in which the fetched headers are separated from one another based on similarities between respective pairs of the fetched headers; for each of one or more of the clusters, by network node retrieving, from one or more of the messaging servers, a respective sample of the electronic messages in the population that are associated with the fetched headers in the cluster and stored on one or more of the network data storage systems (122, 124, 126); classifying, with a machine learning classifier, each electronic message in the retrieved sample with a respective label from a predefined set of labels comprising one or more purchase related labels and an associated confidence level to create a respective classification data set for the cluster; assigning to the cluster a respective cluster label selected from the predefined set of labels based on at least one cluster classification rule that maps the respective classification data set to the respective cluster label; by network node retrieving, from one or more of the messaging servers, a respective sample of the electronic messages in the population that are associated with the fetched headers in the cluster and stored on one or more of the network data storage systems (122, 124, 126); classifying, with a machine learning classifier, each electronic message in the retrieved sample with a respective label from a predefined set of labels comprising one or more purchase related labels and an associated confidence level to create a respective classification data set for the cluster; assigning to the cluster a respective cluster label selected from the predefined set of labels based on at least one cluster classification rule that maps the respective classification data set to the respective cluster label; for each of one or more clusters assigned a respective one of the purchase related labels, automatically generating a respective filter for filtering purchase related electronic messages, and installing, by a processor, one or more of the filters in at least one network communication channel to select purchase related electronic messages from a set of electronic messages stored in association with respective user accounts on one or more network data storage systems (122, 124, 126) managed by one or more messaging servers.