Fast and Compact Cover Extraction from Big Formal Contexts

Gmati, Haifa; Mouakher, Amira

doi:10.1109/wetice.2018.00047

Cited by 1 publication

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The major drawback of this approach is scalability. Another related work is [20] which investigated the same problem using bipartite graphs. The authors proposed a new algorithm called FAST-COVER which provided a concise conceptual coverage using the graph structure.…”

Section: Full-coverage Approachesmentioning

confidence: 99%

Conceptual Coverage Driven by Essential Concepts: A Formal Concept Analysis Approach

Mouakher

Ragobert²,

Gerin³

et al. 2021

Mathematics

Self Cite

View full text Add to dashboard Cite

Formal concept analysis (FCA) is a mathematical theory that is typically used as a knowledge representation method. The approach starts with an input binary relation specifying a set of objects and attributes, finds the natural groupings (formal concepts) described in the data, and then organizes the concepts in a partial order structure or concept (Galois) lattice. Unfortunately, the total number of concepts in this structure tends to grow exponentially as the size of the data increases. Therefore, there are numerous approaches for selecting a subset of concepts to provide full or partial coverage. In this paper, we rely on the battery of mathematical models offered by FCA to introduce a new greedy algorithm, called Concise, to compute minimal and meaningful subsets of concepts. Thanks to its theoretical properties, the Concise algorithm is shown to avoid the sluggishness of its competitors while offering the ability to mine both partial and full conceptual coverage of formal contexts. Furthermore, experiments on massive datasets also underscore the preservation of the quality of the mined formal concepts through interestingness measures agreed upon by the community.

show abstract

Section: Full-coverage Approachesmentioning

confidence: 99%