5. A sentence classification method comprising: generating, by using a computer, a Document Term (DT) matrix two-dimensionally expressing a relationship between each document contained in a document set and each term of a term list having a plurality of terms each comprising not less than one word; generating a transformed DT matrix having respective clusters, each cluster having, one or more blocks of associated documents, by transforming the DT matrix on a basis of a DM decomposition method used in a graph theory to enable document classification without having to preselect cluster categories; generating classifications associated with the document set on a basis of a relationship between each respective cluster on the transformed DT matrix and said each document classified according to the respective clusters, wherein the generating comprises a virtual representative document generation step of generating a virtual representative document, for each respective cluster on the transformed DT matrix, from a term of each document belonging to the respective cluster; generating a large classification of documents from each document in a bottom-up manner by repeatedly performing, at each DT matrix transformation, said DM decomposition method used to hierarchically cluster documents by setting said DT matrix generated in said DT matrix generation step in an initial state, generating a virtual representative document in a virtual representative document generation step for each respective cluster on the transformed DT matrix generated from the DT matrix in the DT matrix transformation step, the step of generating a new DT matrix used for next hierarchical clustering processing by adding the virtual representative document to the transformed DT matrix and deleting documents belonging to the cluster of the virtual representative document from the transformed DT matrix, and the step of outputting, for said each respective cluster, information associated with the documents constituting the respective cluster as large classification data of one or more cluster categories; and adding or deleting an arbitrary term with respect to the term list; and the step of generating DT matrices by using term lists before and after edition, and generating and outputting an index indicating validity of the edition from the DT matrices, and in large classification label generation, if a virtual representative document is contained in a given cluster of the respective clusters obtained by the clustering processing, generating a label of the given cluster on which the virtual representative document is based from a term strongly connected to the virtual representative document subsequent to classification of the documents into the respective clusters, wherein in the large classification generation step, repetition of the clustering processing is terminated when no cluster is obtained from the transformed DT matrix in the clustering processing.
5. A sentence classification method comprising: generating, by using a computer, a Document Term (DT) matrix two-dimensionally expressing a relationship between each document contained in a document set and each term of a term list having a plurality of terms each comprising not less than one word; generating a transformed DT matrix having respective clusters, each cluster having, one or more blocks of associated documents, by transforming the DT matrix on a basis of a DM decomposition method used in a graph theory to enable document classification without having to preselect cluster categories; generating classifications associated with the document set on a basis of a relationship between each respective cluster on the transformed DT matrix and said each document classified according to the respective clusters, wherein the generating comprises a virtual representative document generation step of generating a virtual representative document, for each respective cluster on the transformed DT matrix, from a term of each document belonging to the respective cluster; generating a large classification of documents from each document in a bottom-up manner by repeatedly performing, at each DT matrix transformation, said DM decomposition method used to hierarchically cluster documents by setting said DT matrix generated in said DT matrix generation step in an initial state, generating a virtual representative document in a virtual representative document generation step for each respective cluster on the transformed DT matrix generated from the DT matrix in the DT matrix transformation step, the step of generating a new DT matrix used for next hierarchical clustering processing by adding the virtual representative document to the transformed DT matrix and deleting documents belonging to the cluster of the virtual representative document from the transformed DT matrix, and the step of outputting, for said each respective cluster, information associated with the documents constituting the respective cluster as large classification data of one or more cluster categories; and adding or deleting an arbitrary term with respect to the term list; and the step of generating DT matrices by using term lists before and after edition, and generating and outputting an index indicating validity of the edition from the DT matrices, and in large classification label generation, if a virtual representative document is contained in a given cluster of the respective clusters obtained by the clustering processing, generating a label of the given cluster on which the virtual representative document is based from a term strongly connected to the virtual representative document subsequent to classification of the documents into the respective clusters, wherein in the large classification generation step, repetition of the clustering processing is terminated when no cluster is obtained from the transformed DT matrix in the clustering processing. 6. The sentence classification method according to claim 5 , characterized by further comprising the step of outputting each term strongly connected to each document belonging to said arbitrary cluster as a label indicating a classification of the cluster.