3. A method for organizing semi-structured data into a taxonomy based on Tag-Mixed (TM) clustering, said method comprising: retrieving data samples including said semi-structured data, said semi-structured data comprising structured data including structured data fields and tags, and unstructured data; generating a vocabulary of items from said semi-structured data based on 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; adding all possible values of one or more structured attribute types and said unstructured attribute type to said generated vocabulary, wherein each item of said generated vocabulary comprises a set of tokens corresponding to a data sample that is part of said generated vocabulary; initializing an inverted index for said generated vocabulary; for each said data sample, determining said structured and unstructured attribute type and adding said set of tokens associated with each of said structured and unstructured attribute types to said inverted index; clustering said semi-structured data by applying monothetic clustering to said items in said generated vocabulary and said data samples, corresponding to one or more tokens, of said semi-structured data that comprise said items to provide a plurality of clusters; ranking said plurality of clusters with respect to each said structured attribute type and said 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; and outputting data samples, based on said monothetic clustering of said items and said data samples, as said organizing to a user.
6. The method of claim 3 , wherein said clustering is performed using a monothetic clustering algorithm based on each of said attribute types.