From preferences over objects to preferences over concepts
We present an approach to preference modeling and learning preferences from data based on formal concept analysis. We consider techniques to derive preferences over attribute subsets from preferences over objects, including ceteris paribus preferences.
A novel approach to triclustering of a three-way binary data is proposed. Tricluster is defined in terms of Triadic Formal Concept Analysis as a dense triset of a binary relation Y , describing relationship between objects, attributes and conditions. This definition is a relaxation of a triconcept notion and makes it possible to find all triclusters and triconcepts contained in triclusters of large datasets. This approach generalizes the similar study of concept-based biclustering.
The problem of detecting terms that can be interesting to the advertiser is considered. If a company has already bought some advertising terms which describe certain services, it is reasonable to find out the terms bought by competing companies. A part of them can be recommended as future advertising terms to the company. The goal of this work is to propose better interpretable recommendations based on FCA and association rules.
This book constitutes the proceedings of the 14th International Conference on Formal Concept Analysis, ICFCA 2017, held in Rennes, France, in June 2017. The 13 full papers presented in this volume were carefully reviewed and selected from 37 submissions. The book also contains an invited contribution and a historical paper translated from German and originally published in “Die Klassifkation und ihr Umfeld”, edited by P. O. Degens, H. J. Hermes, and O. Opitz, Indeks-Verlag, Frankfurt, 1986. The field of Formal Concept Analysis (FCA) originated in the 1980s in Darmstadt as a subfield of mathematical order theory, with prior developments in other research groups. Its original motivation was to consider complete lattices as lattices of concepts, drawing motivation from philosophy and mathematics alike. FCA has since then developed into a wide research area with applications much beyond its original motivation, for example in logic, data mining, learning, and psychology.
A vast amount of documents in the Web have duplicates, which is a challenge for developing efficient methods that would compute clusters of similar documents. In this paper we use an approach based on computing (closed) sets of attributes having large support (large extent) as clusters of similar documents. The method is tested in a series of computer experiments on large public collections of web documents and compared to other established methods and software, such as biclustering, on same datasets. Practical efficiency of different algorithms for computing frequent closed sets of attributes is compared.
The consideration of symbolic machine learning algorithms as an entire class will make it possible, in the future, to generate algorithms, with the aid of some parameters, depending on the initial users’ requirements and the quality of solving targeted problems in domain applications.
Diagnostic Test Approaches to Machine Learning and Commonsense Reasoning Systems surveys, analyzes, and compares the most effective algorithms for mining all kinds of logical rules. Global academics and professionals in related fields have come together to create this unique knowledge-sharing resources which will serve as a forum for future collaborations.