Distance-based clustering of sparsely observed stochastic processes, with applications to online auctions

TL;DR

This paper introduces a novel distance measure for sparsely observed stochastic processes, enabling clustering and analysis of irregular longitudinal data, with applications to online auction bidding patterns.

Contribution

It proposes a new distance metric for sparse, irregular data and demonstrates its effectiveness in clustering online auction bidding behaviors.

Findings

Identified six distinct bidding pattern clusters

Linked bidding patterns to auction price outcomes

Applied clustering to real eBay auction data

Abstract

We propose a distance between two realizations of a random process where for each realization only sparse and irregularly spaced measurements with additional measurement errors are available. Such data occur commonly in longitudinal studies and online trading data. A distance measure then makes it possible to apply distance-based analysis such as classification, clustering and multidimensional scaling for irregularly sampled longitudinal data. Once a suitable distance measure for sparsely sampled longitudinal trajectories has been found, we apply distance-based clustering methods to eBay online auction data. We identify six distinct clusters of bidding patterns. Each of these bidding patterns is found to be associated with a specific chance to obtain the auctioned item at a reasonable price.