Abstract
Appropriate data mining exploration methods can reveal valuable but hidden information in today’s large quantities of transactional data. While association rules generation is commonly used for transactional data analysis, clustering is rather rarely used for analysis of this type of data. In this paper we provide adaptations of parameters related to association rules generation so they can be used to represent distance. Furthermore, we integrate goal-oriented quantitative attributes in distance measure formulation to increase the quality of gained results and streamline the decision making process. As a proof of concept, newly developed measures are tested and results are discussed both on a referent dataset as well as a large real-life retail dataset.
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Vranić, M., Pintar, D., Skočir, Z. (2012). Integrating Quantitative Attributes in Hierarchical Clustering of Transactional Data. In: Jezic, G., Kusek, M., Nguyen, NT., Howlett, R.J., Jain, L.C. (eds) Agent and Multi-Agent Systems. Technologies and Applications. KES-AMSTA 2012. Lecture Notes in Computer Science(), vol 7327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30947-2_13
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DOI: https://doi.org/10.1007/978-3-642-30947-2_13
Publisher Name: Springer, Berlin, Heidelberg
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