Clustering on Ranked Data for Campaign Selection

Recently the ranked data are commonly seen in the era of Internet and e-commerce where the consumers give their opinion in the form of ranks of a set of items online. The consumers are asked to put the ranks on items according to their order of preference. The applications of clustering ranked data are target marketing, campaign selection, top
$k$
-items, etc. The objective of this paper is to implement campaign selection process using clustering feedback data which are in rank ordered. To implement the proposed method we divide our experiments into two parts first, group the ranked data (consumer feedback), by applying different distance calculations, e.g., Kendall’s tau, Spearman’s rho square, Spearman’s footrule, Cayley’s distance. Second, use the knowledge derived from the groups in campaign selection process. We have compared our proposed clustering algorithm with existing algorithms on different real datasets, and results showed the effectiveness of our proposed algorithm.