Formal Concept Analysis in Knowledge Discovery: A Survey

Abstract

Abstract.In this paper, we analyze the literature on Formal Concept Analysis (FCA) using FCA. We collected 702 papers published between 2003-2009 mentioning Formal Concept Analysis in the abstract. We developed a knowledge browsing environment to support our literature analysis process. The pdf-files containing the papers were converted to plain text and indexed by Lucene using a thesaurus containing terms related to FCA research. We use the visualization capabilities of FCA to explore the literature, to discover and conceptually represent the main research topics in the FCA community. As a case study, we zoom in on the 140 papers on using FCA in knowledge discovery and data mining and give an extensive overview of the contents of this literature.

Interactive hierarchical tag clouds for summarizing spatiotemporal social contents

“…rate mis = num mis num tp = num f p + num f n num tp (6) For case two in Fig. 5(b), P f n is empty because biclusters in any partition overlapping the geographic region R geo are merged.…”

“…To tackle the challenges, we propose an efficient biclustering approach based on formal concept analysis [5], [6]. The results are called biclusters, and each consists of a set of tags (keywords in contents) and a set of contents, where the tags frequently co-occurs in the contents.…”

“…They proposed to do biclustering by means of formal concept analysis (FCA). FCA [5], [6] is a data analysis method in growing popularity across various domain, which is widely used to discover relationships between a set of objects and a set of attributes. Although biclustering using FCA is more efficient than general biclustering approaches, this approach is still unable to scale to huge amounts of social network data.…”

Publication Analysis of the Formal Concept Analysis Community

“…Poelmans et al combine text mining and FCA to provide a survey on the FCA literature related to knowledge discovery [10] (140 publications) and information retrieval [11] (103 publications). Using a thesaurus of relevant terms, the retrieved papers are classified and visualized using a concept lattice.…”

Parallelization of the next Closure algorithm for generating the minimum set of implication rules

“…[1][2][3] Its formalization was born in 1982 with the work of Wille, [4] who proposed considering each lattice element as a formal concept and the lattice itself as representing a conceptual hierarchy. [5] However, the applicability of FCA is limited by its capacity of dealing with huge data sets.…”