The process of knowledge discovery in databases
Advances in knowledge discovery and data mining
Faithful Representations and Topographic Maps: From Distortion- to Information-Based Self-Organization
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis on Its Way from Mathematics to Computer Science
ICCS '02 Proceedings of the 10th International Conference on Conceptual Structures: Integration and Interfaces
Conceptual Knowledge Discovery in Databases Using Formal Concept Analysis Methods
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
An Exploration into the Power of Formal Concept Analysis for Domestic Violence Analysis
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Formal concept analysis in information science
Annual Review of Information Science and Technology
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Formal concept analysis in knowledge discovery: a survey
ICCS'10 Proceedings of the 18th international conference on Conceptual structures: from information to intelligence
An iterative requirements engineering framework based on Formal Concept Analysis and C-K theory
Expert Systems with Applications: An International Journal
Human-centered text mining: a new software system
ICDM'12 Proceedings of the 12th Industrial conference on Advances in Data Mining: applications and theoretical aspects
Review: Formal concept analysis in knowledge processing: A survey on applications
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
In this paper, we propose a framework for iterative knowledge discovery from unstructured text using Formal Concept Analysis and Emergent Self Organizing Maps. We apply the framework to a real life case study using data from the Amsterdam-Amstelland police. The case zooms in on the problem of distilling concepts for domestic violence from the unstructured text in police reports. Our human-centered framework facilitates the exploration of the data and allows for an efficient incorporation of prior expert knowledge to steer the discovery process. This exploration resulted in the discovery of faulty case labellings, common classification errors made by police officers, confusing situations, missing values in police reports, etc. The framework was also used for iteratively expanding a domain-specific thesaurus. Furthermore, we showed how the presented method was used to develop a highly accurate and comprehensible classification model that automatically assigns a domestic or non-domestic violence label to police reports.