Spectral Clustering: An empirical study of Approximation Algorithms and its Application to the Attrition Problem
For practitioners needing scalable clustering, this paper provides an empirical comparison of approximate spectral clustering methods and shows their applicability to a specific business problem (attrition), though the results are incremental.
This paper empirically evaluates several approximation methods for spectral clustering to reduce computational cost, and applies spectral clustering to the employee attrition problem. The study identifies effective approximations and demonstrates their utility in a business context.
Clustering is the problem of separating a set of objects into groups (called clusters) so that objects within the same cluster are more similar to each other than to those in different clusters. Spectral clustering is a now well-known method for clustering which utilizes the spectrum of the data similarity matrix to perform this separation. Since the method relies on solving an eigenvector problem, it is computationally expensive for large datasets. To overcome this constraint, approximation methods have been developed which aim to reduce running time while maintaining accurate classification. In this article, we summarize and experimentally evaluate several approximation methods for spectral clustering. From an applications standpoint, we employ spectral clustering to solve the so-called attrition problem, where one aims to identify from a set of employees those who are likely to voluntarily leave the company from those who are not. Our study sheds light on the empirical performance of existing approximate spectral clustering methods and shows the applicability of these methods in an important business optimization related problem.