%0 Journal Article %1 dolques2013aocposets %A Dolques, Xavier %A Ber, Florence Le %A Huchard, Marianne %D 2013 %J AOC-posets: a scalable alternative to Concept Lattices for Relational Concept Analysis. CLA: Concept Lattices and their Applications %K aoc-poset concept_lattices data_mining fca rca %P 129-140 %T AOC-Posets: a Scalable Alternative to Concept Lattices for Relational Concept Analysis. %U https://hal.science/hal-00916850 %X Relational Concept Analysis (RCA) is a useful tool for classification and rule discovery on sets of objects with relations. Based on FCA, it pro duces more results than the latter but also an increase in complexity. Besides, in numerous applications of FCA, AOC-posets are used rather than lattices in order to reduce combinatorial problems. An AOC-poset is a subset of the concept lattice considering only concepts introducing an object or an attribute. AOC-posets are much smaller and easier to compute than concept lattices and still contain the information needed to rebuild the initial data. This paper introduces a modification of the RCA process based on AOC-posets rather than concept lattices. This work is motivated by a big set of relational data on river streams to be analysed. We show that using AOC-poset on these data provides a reasonable concept number.

@article{dolques2013aocposets, abstract = {Relational Concept Analysis (RCA) is a useful tool for classification and rule discovery on sets of objects with relations. Based on FCA, it pro duces more results than the latter but also an increase in complexity. Besides, in numerous applications of FCA, AOC-posets are used rather than lattices in order to reduce combinatorial problems. An AOC-poset is a subset of the concept lattice considering only concepts introducing an object or an attribute. AOC-posets are much smaller and easier to compute than concept lattices and still contain the information needed to rebuild the initial data. This paper introduces a modification of the RCA process based on AOC-posets rather than concept lattices. This work is motivated by a big set of relational data on river streams to be analysed. We show that using AOC-poset on these data provides a reasonable concept number.}, added-at = {2023-06-13T08:19:18.000+0200}, author = {Dolques, Xavier and Ber, Florence Le and Huchard, Marianne}, biburl = {https://www.bibsonomy.org/bibtex/2e3117678f7514c1924b1c4d563751715/kawollek}, description = {AOC-posets: a scalable alternative to Concept Lattices for Relational Concept Analysis - Archive ouverte HAL}, id = {https://hal.science/hal-00916850, hal-00916850, https://hal.science/hal-00916850/document}, interhash = {722fff0d40c4c4c04600fae0bbf69452}, intrahash = {e3117678f7514c1924b1c4d563751715}, journal = {AOC-posets: a scalable alternative to Concept Lattices for Relational Concept Analysis. CLA: Concept Lattices and their Applications}, keywords = {aoc-poset concept_lattices data_mining fca rca}, language = {englisch}, month = oct, pages = {129-140}, timestamp = {2023-06-13T08:19:18.000+0200}, title = {AOC-Posets: a Scalable Alternative to Concept Lattices for Relational Concept Analysis.}, type = {Publication}, url = {https://hal.science/hal-00916850}, year = 2013 }