Extracting Decision Trees from Interval Pattern Concept Lattices

Zainab Assaghir, Mehdi Kaytoue, Wagner Meira Jr., Jean Villerd
2011 International Conference on Concept Lattices and their Applications  
Formal Concept Analysis (FCA) and concept lattices have shown their effectiveness for binary clustering and concept learning. Moreover, several links between FCA and unsupervised data mining tasks such as itemset mining and association rules extraction have been emphasized. Several works also studied FCA in a supervised framework, showing that popular machine learning tools such as decision trees can be extracted from concept lattices. In this paper, we investigate the links between FCA and
more » ... sion trees with numerical data. Recent works showed the efficiency of "pattern structures" to handle numerical data in FCA, compared to traditional discretization methods such as conceptual scaling.
dblp:conf/cla/AssaghirKMV11 fatcat:e4ogybrbfrcurjkwagpoqi2nke