Abstract
Association Rule Mining (ARM) in the context of imperfect data (e.g. imprecise data) has received little attention so far despite the prevalence of such data in a wide range of real-world applications. In this work, we present an ARM approach that can be used to handle imprecise data and derive imprecise rules. Based on evidence theory and Multiple Criteria Decision Analysis, the proposed approach relies on a selection procedure for identifying the most relevant rules while considering information characterizing their interestingness. The several measures of interestingness defined for comparing the rules as well as the selection procedure are presented. We also show how a priori knowledge about attribute values defined into domain taxonomies can be used to (i) ease the mining process, and to (ii) help identifying relevant rules for a domain of interest. Our approach is illustrated using a concrete simplified case study related to humanitarian projects analysis.
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Notes
- 1.
Note that the simplification of the mining process here refers to a reduction of complexity in terms of the number of rules analysed, i.e. search space size. Algorithmic contributions and therefore complexity analyses regarding efficient implementations of the proposed approach are left for future work.
- 2.
Indeed all the measures used in our approach take values in the interval [0, 1], then a measure k to minimize can be changed to a measure to maximize by considering \(1-g_k(r)\) instead of \(g_k(r)\).
- 3.
Evaluating support and confidence of \(\overline{A} \rightarrow B\) and \(\overline{A} \rightarrow \overline{B}\) can lead to undefined values, e.g. evaluating \(\overline{A} \rightarrow B\), we have \(Bel(\overline{A} \times B) = 0\) when \(\overline{A}\) has never been observed, leading to \(Bel(B |\overline{A})\) being undefined. However, pruning using dominance and Electre I requires the same measures to be defined. Undefined values are thus substituted by an arbitrary value that neither favor nor penalize the evaluation of the rule \(A \rightarrow B\). The median of \(Bel(\overline{A} \times B)\) (resp. \(Bel(\overline{A} \times \overline{B})\)) has been chosen. Note that \(A \rightarrow \overline{B}\) is not concerned since evaluating \(A\rightarrow B\) implies evidence on A.
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L’Héritier, C., Harispe, S., Imoussaten, A., Dusserre, G., Roig, B. (2019). Selecting Relevant Association Rules From Imperfect Data. In: Ben Amor, N., Quost, B., Theobald, M. (eds) Scalable Uncertainty Management. SUM 2019. Lecture Notes in Computer Science(), vol 11940. Springer, Cham. https://doi.org/10.1007/978-3-030-35514-2_9
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