1. A method for cross-guided data clustering by aligning target clusters in a target domain to source clusters in a source domain, said method comprising: a processor of a computer system receiving the source domain and the target domain, wherein the source domain comprises at least one source data item and the target domain comprises at least one target data item; generating source centroids that respectively represent each source cluster of the source domain, wherein each source centroid of said source centroids is projected to at least one common attribute that is shared by said at least one source data item and said at least one target data item; initializing target centroids within the target domain, wherein said target centroids respectively represent each target cluster of the target domain; aligning the target centroids to the source centroids; and transmitting the aligned target centroids to an output device of the computer system.
2. The method of claim 1 , said generating the source centroids comprising: establishing a pivot vocabulary, wherein a common attribute of said at least one common attribute is word, the pivot vocabulary being defined as a first set of lexically identical words appearing in both the source domain and the target domain, wherein said at least one source data item and said at least one target data item is selected from a set of documents; creating a projection matrix of a non-pivot vocabulary to the pivot vocabulary such that the projection matrix represents weights associated with the non-pivot vocabulary in terms of weights associated with the pivot vocabulary, wherein the non-pivot vocabulary is a second set of words appearing in either the source domain or the target domain but not both domains; assigning said at least one source data item to a respective source centroid that has a smallest distance value to each source data item of said at least one source data item, wherein the respective source centroid represents said each source cluster; and calculating a respective weight of a pivot word appearing in the respective source centroid by projecting a weight of a non-pivot word that is relevant to the pivot word, for each pivot word in the pivot vocabulary, wherein the non-pivot word is in the non-pivot vocabulary.