Knowledge extraction based on fuzzy unsupervised decision tree: application to an emergency call center

Authors:
Francisco Barrientos;Gregorio Sainz
Affiliations:
CARTIF Centro Tecnológico, Valladolid, Spain;University of Valladolid, Systems Engineering and Control Department School of Industrial Engineering, Valladolid, Spain and CARTIF Centro Tecnológico, Valladolid, Spain
Venue:
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Year:
2010

Citing 12
Cited 0

Simplifying decision trees

International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
A Validity Measure for Fuzzy Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Designing a Call Center with Impatient Customers

Manufacturing & Service Operations Management
Data Mining: Concepts and Algorithms From Multimedia to Bioinformatics

Data Mining: Concepts and Algorithms From Multimedia to Bioinformatics
Interpretable Hierarchical Clustering by Constructing an Unsupervised Decision Tree

IEEE Transactions on Knowledge and Data Engineering
Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling

Fuzzy Sets and Systems
Feature Selection for Time Series Forecasting: A Case Study

HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
The Top Ten Algorithms in Data Mining

The Top Ten Algorithms in Data Mining
Identifying critical variables of principal components for unsupervised feature selection

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Validity-guided (re)clustering with applications to image segmentation

IEEE Transactions on Fuzzy Systems
Generating an interpretable family of fuzzy partitions from data

IEEE Transactions on Fuzzy Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper describes the application of a fuzzy version of Unsupervised Decision Tree (UDT) to the problem of an emergency call center. The goal is to obtain a decision support system that helps in the resource planning, reaching a trade-off between efficiency and quality of service. To reach this objective, the different types of days have been characterized based on variables that permits available resources assignment in an easy and understandable way. In order to deal with availability of expert knowledge on the problem, an unsupervised methodology had to be used, so fuzzy UDT is a solution merging decision trees and clustering, providing the performance of both viewpoints. Quality indexes give criteria for the selection of a reasonable solution to the complexity, as well as interpretability of the trees and the quality of generated clusters, and also the type of days and the performance from the resources point of view.