The concept of frequent itemset mining for text

3rd Annual Applied Science and Engineering Conference

工业技术;自然科学

Maylawati, D.S.^1

Departement of Informatics, Sekolah Tinggi Teknologi Garut, Jalan Mayor Syamsu No 1, Tarogong Kidul Kabupaten Garut

44151, Indonesia^1

关键词: Apriori algorithms;    Frequent itemset mining;    Itemset mining;    Literature reviews;    Pattern growth;    Structured data;    Text mining;    Unstructured data;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/434/1/012043/pdf
DOI  :  10.1088/1757-899X/434/1/012043

来源: IOP

【 摘 要 】

Frequent itemset mining is one of popular data mining technique with frequent pattern or itemset as representation of data. However, most of frequent itemset mining research was conducted for structured data. In this paper, we did literature review of the frequent itemset mining algorithm that suitable for unstructured data such as text data. We reviewed several frequent itemset mining algorithm that had already used in text mining research, among others Apriori algorithm; Pattern-growth algorithm; and various algorithm for itemset mining problem such as based on representation, database changes, and richer database type. The result showed that from year to year research on text data using frequent itemset mining had increased, including the development of frequent itemset mining algorithms. Although, still rarely new algorithms were implemented in text data.