1. A computer-implemented method for advance and/or unsupervised machine learning by Latent Metonymical Analysis and Indexing (LMai), said method comprising steps of: a. inputting natural documents; b. eliminating special characters to count a number of words within the given document, filtering the contents of the document based on a list of predefined stop-words and calculating a fraction of the stop-words present in the document; c. determining a Significant Single Value Term data set and a Significant Multi Value Term data set from the document; d. decomposing words in the Significant Single Value Term data set and the Significant Multi Value Term data set to extract Keywords of the document being processed; e. optionally, determining KeyTerms and their respective hand-in-hand (HiH) words automatically for further decomposition, wherein the hand-in-hand (HiH) words are words that go together as one word; f. identifying a Topic in an unsupervised manner based not just on a File Name but also by manipulating/comparing with various combinations of document attributes that are extracted to select Best Topic candidates and thereafter defining an appropriate Topic based on predefined rules using a computer; and g. analyzing relationship between the identified Topics and the Keywords and thereafter indexing the Topics and their related Keywords, KeyTerms and their respective hand-in-hand terms into a Metonymy cluster and a KeyTerms HiH cluster respectively.