Constraint-based Mining of Fault Tolerant Patterns from Boolean Data

International audienc

Constraint-based mining of fault-tolerant patterns from Boolean data

International audienc

Constraint-based Mining of Fault Tolerant Patterns from Boolean Data

International audienc

Constraint-based mining of fault-tolerant patterns from Boolean data

Abstract. Thanks to an important research effort the last few years, in-ductive queries on local patterns (e.g., set patterns) and complete solvers which can evaluate them on large data sets have been proved extremely useful. The more we use such queries on real-life data, e.g., biological data (and thus intrinsically dirty and noisy), the more we are convinced that inductive queries should return fault-tolerant patterns. In this work, we consider user-defined constraints for a declarative specification of fault-tolerance. We discuss the design of such constraints on bi-sets extracted from Boolean data sets. Our starting point is the fundamental limita-tion of formal concept discovery (i.e., closed set mining) from noisy data and we propose a constraint-based mining approach for relevant fault-tolerant bi-set mining. Formalizing three recent proposals, our frame-work enables a better understanding of the needed trade-off between extraction feasibility, completeness, relevancy, and ease of interpretation of these fault-tolerant patterns. An original empirical evaluation on both synthetic and real-life medical data is given. It enables a comparison of the various proposals and it motivates further directions of research.

FCAIR 2012 Formal Concept Analysis Meets Information Retrieval Workshop co-located with the 35th European Conference on Information Retrieval (ECIR 2013) March 24, 2013, Moscow, Russia

International audienceFormal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classifiation. The area came into being in the early 1980s and has since then spawned over 10000 scientific publications and a variety of practically deployed tools. FCA allows one to build from a data table with objects in rows and attributes in columns a taxonomic data structure called concept lattice, which can be used for many purposes, especially for Knowledge Discovery and Information Retrieval. The Formal Concept Analysis Meets Information Retrieval (FCAIR) workshop collocated with the 35th European Conference on Information Retrieval (ECIR 2013) was intended, on the one hand, to attract researchers from FCA community to a broad discussion of FCA-based research on information retrieval, and, on the other hand, to promote ideas, models, and methods of FCA in the community of Information Retrieval