{'fraction_non_alphanumeric': 0.019008264462809916, 'fraction_numerical': 0.0, 'mean_word_length': 5.178571428571429, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Some Links Between Decision Tree and Dichotomic Lattice

There are two types of classification methods using a Galois lattice: as most of them rely on selection, recent research work focus on navigation- based approaches. In navigation-oriented methods, classification is performed by navigating through the complete lattice, similar to the decision tree. When de- fined from binary attributes obtained after a discretization pre-processing step, and more generally when a non-empty set of complementarity attributes can be associated to each binary attribute, lattices are denoted as "dichotomic lattices". The Navigala approach is a navigation-based classification method that relies on the use of a dichotomic lattice. It was initially proposed for symbol recognition in the field of technical document image analysis. In this paper, we define the structural links between decision trees and dichotomic lattices defined from the same table of data described by binary attributes. Under this condition, we prove both that every decision tree is included in the dichotomic lattice and that the dichotomic lattice is the merger of all the decision trees that can be constructed from the same binary data table.