Mining Substitution Rules: A Knowledge-based Approach using Dynamic Ontologies

Rupal Sethi; B. Shekar

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Paper

Mining Substitution Rules: A Knowledge-based Approach using Dynamic Ontologies

Topics: Data Mining; Knowledge-Based Systems; Ontologies

In Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, 73-84, 2018 , Funchal, Madeira, Portugal

Authors: Rupal Sethi and B. Shekar

Affiliation: Indian Institute of Management Bangalore, India

Keyword(s): Substitution Rules, Interestingness, Affordances, Dynamic Ontology, Market Basket.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Data Mining ; Databases and Information Systems Integration ; e-Business ; Enterprise Engineering ; Enterprise Information Systems ; Enterprise Ontologies ; Formal Methods ; Knowledge-Based Systems ; Ontologies ; Sensor Networks ; Signal Processing ; Simulation and Modeling ; Soft Computing ; Symbolic Systems

Abstract: Association Rule Mining has so far focused on generating and pruning positive rules using various interestingness measures. However, there are very few studies that explore the mining process of substitution rules. These studies have incorporated a limited definition of substitution, either in statistical terms or based on manager’s static knowledge. Here we attempt to provide a customer-centric model of substitution rule mining using the lens of affordance. We adopt a knowledge-based approach involving a dynamic ontology wherein objects are positioned based on the affordances they are preferred for. This contrasts with the traditional static ontology approach that highlights manager’s static knowledge base. We develop an Expected-Actual Substitution Framework to compare relatedness between items in the static and dynamic ontologies. We present Affordance-Based Substitution (ABS) algorithm to mine substitution rules based on the proposed approach. We also come up with a novel interes tingness measure that enhances the quality of our substitution rules thus leading to effective knowledge discovery. Empirical analyses are performed on a real-life supermarket dataset to show the efficacy of ABS algorithm. We compare the generated rules with those generated by another substitution rule mining algorithm from the literature. Our results show that substitution rules generated through ABS algorithm capture customer perceptions that are generally missed by alternate approaches. (More)

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Paper citation in several formats:

Sethi, R. and Shekar, B. (2018). Mining Substitution Rules: A Knowledge-based Approach using Dynamic Ontologies. In Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-275-2; ISSN 2184-433X, SciTePress, pages 73-84. DOI: 10.5220/0006577400730084

@conference{icaart18,
author={Rupal Sethi. and B. Shekar.},
title={Mining Substitution Rules: A Knowledge-based Approach using Dynamic Ontologies},
booktitle={Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2018},
pages={73-84},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006577400730084},
isbn={978-989-758-275-2},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Mining Substitution Rules: A Knowledge-based Approach using Dynamic Ontologies
SN - 978-989-758-275-2
IS - 2184-433X
AU - Sethi, R.
AU - Shekar, B.
PY - 2018
SP - 73
EP - 84
DO - 10.5220/0006577400730084
PB - SciTePress