Text Mining Infrastructure in Erlang

Abbas Jkhayyir Kadhim


Nowadays, one of the most important aspects of our lives is intrinsic pillars, which are the fastest element to implement in order to obtain an efficient result and support decision making. This paper proves the fastest method of classification algorithm performance during the extraction of data knowledge. While analyzing the text file description, data mining extraction for the text file is always used in conjunction with various fields, such as natural language processing (NLP) and information data. The preparation of the text mining algorithms for the text file is based on two files that are derived from Erlang shell framework. The first file is the text file, which requires interpretation and results, while the second file contains the actual words from the English dictionary. Inter-process communication is required to obtain a result file on the classified data according to the correct or incorrect words in the English language. We also present analysis techniques using methods based on text count, text classification, and string kernels. The article provides two deductive tips, with the information retrieval data of the text file including the percentage of correct or incorrect words. Information retrieval must be performed as a Knowledge Discovery in Databases (KDD), using the frequent pattern analysis technique under the classification algorithm to provide us with a full useful analysis of the text file. Furthermore, the result test under the runtime demonstrates the code written by Erlang functional programming language, which provides high efficiency in the speed of its performance in implementation.


Keywords: machine learning, text mining, supervised learning, classification algorithm, Erlang functional programming language.

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