What is text distance in data mining I need to write a report on visualization of multidimensional data, map and text distance.
I got content related to other two but not getting any clue about text distance. 
Is it related to Data visualization?
 A: Text distance can be related to visualization in that a similarity matrix of a text string, words or linguistic concepts can be represented in a low dimensional space that is suitable for mapping. 
The key thing is the metric basis for those similarities. The distance metrics are many. One good introduction to these concepts is Levenshtein distance. Wikipedia defines it like this:

In information theory and computer science, the Levenshtein distance
  is a string metric for measuring the difference between two sequences.
  Informally, the Levenshtein distance between two words is the minimum
  number of single-character edits (i.e. insertions, deletions or
  substitutions) required to change one word into the other. Levenshtein
  distance may also be referred to as edit distance, although that may
  also denote a larger family of distance metrics.

The problem with this metric is its computational inefficiency.
The "larger family of distance metrics" mentioned above in computational linguistics includes Hamming distance, the Jaccard index, Dice coefficient, and many more. Search the Wiki entries for Hamming or Jaccard -- both provide links to the rich number of metrics available for analysis.
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A recent academic publication Semantic Similarity from Natural Language and Ontology reviews the thinking behind semantic measures. It is one of the most rigorous treatments related to your query that can be found:
http://www.amazon.com/Semantic-Similarity-Language-Synthesis-Technologies/dp/1627054464/ref=sr_1_1?ie=UTF8&qid=1459515337&sr=8-1&keywords=Semantic+Similarity+from+Natural+Language+and+Ontology
