Easy Web Search Results Clustering: When Baselines Can Reach State-of-the-Art Algorithms

Abstract

This work discusses the evaluation of baseline algorithms for Web search results clustering. An analysis is performed over frequently used baseline algorithms and standard datasets. Our work shows that competitive results can be obtained by either fine tuning or performing cascade clustering over well-known algorithms. In particular, the latter strategy can lead to a scalable and real-world solution, which evidences comparative results to recent text-based state-of-the-art algorithms.