ONTROX co.,Ltd.

Text analysis is going to be more important with Big Data Analytics But so far it takes enormous time and resources , even impossible.
MFA can execute text analysis/clustering with Big Data at high velocity.

What’s MFA
  • MFA is used AS
    Cleaning/filtering text data that spoil unrelated group of text (eg.advertisement,spam)
    Clustering users by relevancy of text’s context or meaning
    Auto indexing of text
    Classifications of users/text with data scientist’s definitions

    MFA is totally machine based analytics
    Why IDG is superior

How MFA works
  • 1, Classification Human direction / system executionby teaching sets of data
    2,Clustering Machine indexing / clustering
    *with teaching sets / without teaching sets
    Why IDG is superior
Why MFA important
  • Motivation Suppose we have a set of English text documents and wish to determine which document is most relevant to the query “the brown cow”. A simple way to start out is by eliminating documents that do not contain all three words “the”, “brown”, and “cow”, but this still leaves many documents. To further distinguish them, we might count the number of times each term occurs in each document and sum them all together; the number of times a term occurs in a document is called its term frequency.
    However, because the term “the” is so common, this will tend to incorrectly emphasize documents which happen to use the word “the” more frequently, without giving enough weight to the more meaningful terms “brown” and “cow”. The term “the” is not a good keyword to distinguish relevant and non-relevant documents and terms, unlike the less common words “brown” and “cow”. Hence an inverse document frequency factor is incorporated which diminishes the weight of terms that occur very frequently in the document set and increases the weight of terms that occur rarely.
Why MFA is superior
  • Velocity research of MFA
    Why IDG is superior
Twitter Analytics