Abstract — Document clustering has been a particularly active research field within the Information Retrieval (IR) community. Among the numerous clustering algorithms proposed, single-pass clustering stands out in terms of both time and space efficiency. However, it is generally acknowledged that single-pass clustering has a major defect, namely its output depends on the order in which documents are presented. Building on our previous work, and having identified single-pass clustering as potentially useful for P2P IR, we study the extent to which this is true in practical terms. We do so by experimenting with two large webbased testbeds, which are suitable for Peer-to-Peer IR evaluation. The results of our study show that document ordering does not practically matter for single-pass clustering. I.

Automatic classification of scientific articles based on common characteristics is an interesting problem with many applications in digital library and information retrieval systems. Properly organized articles can be useful for automatic generation of taxonomies in scientific writings, textual summarization, efficient information retrieval etc. Generating article bundles from a large number of input articles, based on the associated features of the articles is tedious and computationally expensive task. In this report we propose an automatic two-step approach for topic extraction and bundling of related articles from a set of scientific articles in real-time. For topic extraction, we make use of Latent Dirichlet Allocation (LDA) topic modeling techniques and for bundling, we make use of hierarchical agglomerative clustering techniques. We run experiments to validate our bundling semantics and compare it with existing models in use. We make use of an online crowdsourcing marketplace provided by Amazon called Amazon Mechanical Turk to carry out experiments. We explain our experimental setup and empirical results in detail and