9. A computer-implemented method for visually suggesting classification for inclusion-based document cluster spines, comprising the steps of: designating a set of reference documents each associated with a classification code; obtaining a different set of un-coded documents; combining one or more of the coded reference documents with a plurality of un-coded documents into a combined document set; grouping the documents in the combined document set into clusters; organizing the clusters along one or more spines, each spine comprising a vector; and providing a visual suggestion for assigning one of the classification codes to one of the spines comprising visually representing each of the reference concepts in the clusters along that spine; identifying one of the documents as a center of one of the clusters; generating a score vector for the cluster center; comparing the score vector for the cluster center to score vectors associated with one or more of the reference documents; identifying a neighborhood of similar reference documents for the cluster based on the comparison; and assigning one of the classification codes to the cluster based on the neighborhood, further comprising: determining a distance between the cluster center and the reference documents in the neighborhood; and generating the classification code for assignment to the cluster, comprising at least one of: identifying the reference document with the closest distance to the cluster center and assigning the classification code of the reference document with the closest distance as the generated classification code for the cluster; calculating an average of the distances between the cluster center and the reference documents associated with each of the classification codes and assigning the classification code with the closest average distance as the generated classification code of the cluster; and counting the reference documents in the neighborhood for each of the classification codes, weighing each count based on the distance between the reference documents with the classification code and the cluster center, and assigning the classification code with the highest weighted count as the generated classification code of the cluster, wherein the steps are performed by a suitably programmed computer.
9. A computer-implemented method for visually suggesting classification for inclusion-based document cluster spines, comprising the steps of: designating a set of reference documents each associated with a classification code; obtaining a different set of un-coded documents; combining one or more of the coded reference documents with a plurality of un-coded documents into a combined document set; grouping the documents in the combined document set into clusters; organizing the clusters along one or more spines, each spine comprising a vector; and providing a visual suggestion for assigning one of the classification codes to one of the spines comprising visually representing each of the reference concepts in the clusters along that spine; identifying one of the documents as a center of one of the clusters; generating a score vector for the cluster center; comparing the score vector for the cluster center to score vectors associated with one or more of the reference documents; identifying a neighborhood of similar reference documents for the cluster based on the comparison; and assigning one of the classification codes to the cluster based on the neighborhood, further comprising: determining a distance between the cluster center and the reference documents in the neighborhood; and generating the classification code for assignment to the cluster, comprising at least one of: identifying the reference document with the closest distance to the cluster center and assigning the classification code of the reference document with the closest distance as the generated classification code for the cluster; calculating an average of the distances between the cluster center and the reference documents associated with each of the classification codes and assigning the classification code with the closest average distance as the generated classification code of the cluster; and counting the reference documents in the neighborhood for each of the classification codes, weighing each count based on the distance between the reference documents with the classification code and the cluster center, and assigning the classification code with the highest weighted count as the generated classification code of the cluster, wherein the steps are performed by a suitably programmed computer. 10. The method according to claim 9 , further comprising: providing at least one of a presence and an absence of the documents with each of the classification codes in the clusters along that spine; and providing a number of the documents with each of the classification codes in the clusters along that spine, wherein the suggestion includes the number and at least one of the presence and the absence.