Open AccessMining Frequent Itemsets of Novel Characters Based on Association Rules
Author(s) -
Hui Cao,
Jiao Li
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1550/3/032158
Subject(s) - association rule learning , apriori algorithm , computer science , grasp , set (abstract data type) , association (psychology) , data mining , object (grammar) , reading (process) , character (mathematics) , field (mathematics) , information retrieval , artificial intelligence , natural language processing , mathematics , psychology , linguistics , philosophy , geometry , pure mathematics , psychotherapist , programming language
This article belongs to the field of Data Mining. It selected the novel “White Deer Plain” as the research object, using the association rules mining method to research the book characters and show the character of interpersonal relationship. In this paper, weka was used as an auxiliary tool, and Apriori algorithm was used in association rules. The whole work text was input into the database, and each chapter of the article was separated. Through co-occurrence analysis, the name nodes in the text were extracted as keywords, and weight was assigned. Keywords appearing in a chapter were treated as a record, and a list of keywords from all chapters was combined to form a data set. Multiple scans of the database are made, and frequent item sets were found from the constructed data set, and association rules between people were found. The method assists the reader to clarify and grasp the relationship between the characters before reading the full text. It greatly saves the time of reading.