Algorithms for clustering data
Algorithms for clustering data
C4.5: programs for machine learning
C4.5: programs for machine learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Interpretable Hierarchical Clustering by Constructing an Unsupervised Decision Tree
IEEE Transactions on Knowledge and Data Engineering
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
An overview of statistical learning theory
IEEE Transactions on Neural Networks
Survey of clustering algorithms
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
This paper presents a new framework for behavioural modelling that allows to unravel the key drivers that direct specific cognitive behaviours. In order to do so, a novel framework for clustering based on utility measures is presented that allows to understand the different behaviours that different groups of people may have, and allows the creation of profiles that are relevant with respect to the utility measure. The proposed method is not contrary to other clustering methods but rather builds on the functionality of 'basic' clustering algorithms. A common aim of clustering consists of partitioning a set of patterns into different subsets of patterns which have homogeneous characteristics. In this paper we suggest a more ambitious goal that additionally tries to maximize a utility measure. The paper also describes the results obtained when the method is used to analyze human behaviour in the area of customer intelligence. Specifically, the paper analyzes human behaviour with respect to different socio-demographic and economic indicators and allows to uncover the underlying characteristics that may explain the observed cognitive behaviour.