This paper proposes a new class of data mining problems in which agents replace their current object predecessor by another object replacement or successor; the problem is to discover the knowledge used by the agents in identifying suitable successors. While such replacement data is available in many practical applications, in this paper we explore a problem in HR analytics, viz., replacing person in a key position in a project by another most suitable person from other employees. We propose unsupervised distance-based algorithms for finding suitable replacements. We also apply several standard classification techniques. This paper is the first in applying metric learning algorithms to a problem in HR analytics. We compare the approaches using a real-life replacement dataset from a multinational IT company. Results show that metric learning is a promising approach that captures the implicit knowledge for replacement identification.
['Girish Keshav Palshikar', 'Kuleshwar Sahu', 'Rajiv Srivastava']
After You, Who? Data Mining for Predicting Replacements