Machine Learning and Data Analysis Using Posets: A Survey
It provides a comprehensive overview for researchers interested in poset-based methods, but is incremental as it synthesizes prior work.
The paper surveys existing research on applying partially ordered sets (posets) and formal concept analysis to data analysis and machine learning, reviewing theory, algorithms, and applications without presenting new results.
Posets are discrete mathematical structures which are ubiquitous in a broad range of data analysis and machine learning applications. Research connecting posets to the data science domain has been ongoing for many years. In this paper, a comprehensive review of a wide range of studies on data analysis and machine learning using posets are examined in terms of their theory, algorithms and applications. In addition, the applied lattice theory domain of formal concept analysis will also be highlighted in terms of its machine learning applications.