Abstract: Techniques are disclosed that automatically identify and order the most differentiated clusters from a given collection of clusters within a dataset. A measure of dissimilarity is computed for each cluster from a defined reference cluster, and the clusters are ordered according to the chosen dissimilarity. At least N clusters are selected as the most differentiated clusters relative to the defined reference. Within each cluster, the top-M most distinguishing cluster attributes can be automatically identified by an analogous process that computes the dissimilarity of each cluster attribute to its corresponding attribute in the reference cluster, and orders the attributes by dissimilarity. This then allows for automatic surfacing of what it is about a cluster that differentiates its members relative to the population as a whole, and to provide insight on what action or treatment might be made to address that specific segment of the underlying population.

Abstract: Techniques are disclosed that automatically identify and order the most differentiated clusters from a given collection of clusters within a dataset. A measure of dissimilarity is computed for each cluster from a defined reference cluster, and the clusters are ordered according to the chosen dissimilarity. At least N clusters are selected as the most differentiated clusters relative to the defined reference. Within each cluster, the top-M most distinguishing cluster attributes can be automatically identified by an analogous process that computes the dissimilarity of each cluster attribute to its corresponding attribute in the reference cluster, and orders the attributes by dissimilarity. This then allows for automatic surfacing of what it is about a cluster that differentiates its members relative to the population as a whole, and to provide insight on what action or treatment might be made to address that specific segment of the underlying population.

Abstract: Techniques disclosed herein employ entity-activity data expressed in a discrete distribution (histogram) form having one or many dimensions to dynamically classify the entity's usage and/or behavior patterns, where groupings or segmentations of different entities that exhibit similar usage patterns are identified using various approaches, including dimensionality reduction, and/or clustering procedures. A consensus or ensemble clustering may be generated that represents a clustering of clusters, based on subclusterings themselves, and/or any combination of subclusters with entity-activity data to selectively execute a market offering campaign. In one embodiment, the resulting ensemble clusterings enable selective directing of targeted offerings to a telecommunication provider's customers.

Abstract: Techniques disclosed herein employ entity-activity data expressed in a discrete distribution (histogram) form having one or many dimensions to dynamically classify the entity's usage and/or behavior patterns, where groupings or segmentations of different entities that exhibit similar usage patterns are identified using various approaches, including dimensionality reduction, and/or clustering procedures. A consensus or ensemble clustering may be generated that represents a clustering of clusters, based on subclusterings themselves, and/or any combination of subclusters with entity-activity data to selectively execute a market offering campaign. In one embodiment, the resulting ensemble clusterings enable selective directing of targeted offerings to a telecommunication provider's customers.

Abstract: Techniques are disclosed that automatically identify and order the most differentiated clusters from a given collection of clusters within a dataset. A measure of dissimilarity is computed for each cluster from a defined reference cluster, and the clusters are ordered according to the chosen dissimilarity. At least N clusters are selected as the most differentiated clusters relative to the defined reference. Within each cluster, the top-M most distinguishing cluster attributes can be automatically identified by an analogous process that computes the dissimilarity of each cluster attribute to its corresponding attribute in the reference cluster, and orders the attributes by dissimilarity. This then allows for automatic surfacing of what it is about a cluster that differentiates its members relative to the population as a whole, and to provide insight on what action or treatment might be made to address that specific segment of the underlying population.

Abstract: Techniques are disclosed that leverage time series techniques to express entity-activity data in a longitudinal temporal form, which may then be employed to dynamically classify the entity's behavior. In some embodiments, groupings or segmentations of different entities that exhibit similar profiles of longitudinal temporal form are identified using various techniques, including frequency-domain analysis, and/or unsupervised model-based clustering. The clustering of entities enables directing of offerings to, for example, a telecommunication's customer based on characteristics of the cluster.