1. A method for organizing semi-structured data into a taxonomy, based on Tag-Separated (TS) clustering, said method comprising: retrieving documents including said semi-structured data, said semi-structured data comprising structured data including structured data fields and tags, and unstructured data; selecting a structured attribute type including any of a categorical attribute, a numerical attribute, and a tag associated with annotated text, and an unstructured attribute type including a text attribute; clustering said semi-structured data from said retrieved documents into a plurality of clusters based on said selected structured attribute type, wherein for a categorical attribute, each category corresponds to a single cluster; wherein for a numerical attribute, a clustering algorithm clusters numerical data projected onto a range of said numerical attribute; wherein for an annotated text attribute, a monothetic clustering algorithm clusters annotated text data according to tags associated with a vocabulary for said annotated text data; and said selected unstructured attribute type, wherein for said text attribute, a monothetic clustering algorithm clusters text data with respect to said text attribute; ranking said plurality of clusters with respect to each selected structured and unstructured attribute type wherein clusters of said plurality of clusters are ranked based on a criterion comprising coverage provided by a number of data points in a cluster; ranking said selected structured and unstructured attribute types relative to each other based on a ranking measure, wherein said selected structured and unstructured attribute types are ranked based on entropy of corresponding data for each selected structured and unstructured attribute type; and outputting documents, based on said ranking measure and said ranking said plurality of clusters, as said organizing to a user.
1. A method for organizing semi-structured data into a taxonomy, based on Tag-Separated (TS) clustering, said method comprising: retrieving documents including said semi-structured data, said semi-structured data comprising structured data including structured data fields and tags, and unstructured data; selecting a structured attribute type including any of a categorical attribute, a numerical attribute, and a tag associated with annotated text, and an unstructured attribute type including a text attribute; clustering said semi-structured data from said retrieved documents into a plurality of clusters based on said selected structured attribute type, wherein for a categorical attribute, each category corresponds to a single cluster; wherein for a numerical attribute, a clustering algorithm clusters numerical data projected onto a range of said numerical attribute; wherein for an annotated text attribute, a monothetic clustering algorithm clusters annotated text data according to tags associated with a vocabulary for said annotated text data; and said selected unstructured attribute type, wherein for said text attribute, a monothetic clustering algorithm clusters text data with respect to said text attribute; ranking said plurality of clusters with respect to each selected structured and unstructured attribute type wherein clusters of said plurality of clusters are ranked based on a criterion comprising coverage provided by a number of data points in a cluster; ranking said selected structured and unstructured attribute types relative to each other based on a ranking measure, wherein said selected structured and unstructured attribute types are ranked based on entropy of corresponding data for each selected structured and unstructured attribute type; and outputting documents, based on said ranking measure and said ranking said plurality of clusters, as said organizing to a user. 11. The method of claim 1 , wherein said monothetic clustering algorithm generates single level labeled clusters within each attribute, assign documents to a cluster based on a single feature, identify a set of concepts present in each of a collection of documents, wherein said concepts comprise words that appear in said documents and phrases extracted from said documents by natural language processing, and select subsets of said concepts, wherein said subsets are labels of said clusters, and assign documents containing a concept to a cluster comprising said concept as its label.