Web searches from mobile devices such as PDAs and cell phones are becoming increasingly popular. However, the traditional list-based search interface paradigm does not scale well to mobile devices due to their inherent limitations. In this article, we investigate the application of search results clustering, used with some success for desktop computer searches, to the mobile scenario. Building on CREDO (Conceptual Reorganization of Documents), a Web clustering engine based on concept lattices, we present its mobile versions Credino and SmartCREDO, for PDAs and cell phones, respectively. Next, we evaluate the retrieval performance of the three prototype systems. We measure the effectiveness of their clustered results compared to a ranked list of results on a subtopic retrieval task, by means of the device-independent notion of subtopic reach time together with a reusable test collection built from Wikipedia ambiguous entries. Then, we make a crosscomparison of methods (i.e., clustering and ranked list) and devices (i.e., desktop, PDA, and cell phone), using an interactive information-finding task performed by external participants. The main finding is that clustering engines are a viable complementary approach to plain search engines both for desktop and mobile searches especially, but not only, for multitopic informational queries.

Grupowanie opisowe jako metoda eksploracji zbiorów dokumentów tekstowych

The purpose of text clustering in information retrieval is to discover groups of semantically related documents. Accurate and comprehensible cluster descriptions (labels) let the user comprehend the collection’s content faster and are essential for various document browsing interfaces. The task of creating descriptive, sensible cluster labels is difficult—typical text clustering algorithms focus on optimizing proximity between documents inside a cluster and rely on keyword representation for describing discovered clusters. In the approach called Description Comes First (DCF) cluster labels are as important as document groups—DCF promotes machine discovery of comprehensible candidate cluster labels later used to discover related document groups. In this paper we describe an application of DCF to the k-Means algorithm, including results of experiments performed on the 20-newsgroups document collection. Experimental evaluation showed that DCF does not decrease the metrics used to assess the quality of document assignment and offers good cluster labels in return. The algorithm utilizes search engine’s data structures directly to scale to large document collections.

Document clustering is becoming an increasingly popular technique for identifying relationships in unstructured text. In this paper, we attempt to make sense of the output of a clustering algorithm applied to software engineering research papers. We introduce a notion of cluster “stability ” as a measure of the meaningfulness of a cluster. We assess its usefulness and limitations in identifying meaningful clusters. In the process, we track how important research topics may have changed from year to year. 1.

Web clustering engines organize search results by topic, thus offering a complementary view to the flat-ranked list returned by conventional search engines. In this survey, we discuss the issues that must be addressed in the development of a Web clustering engine, including acquisition and preprocessing of search results, their clustering and visualization. Search results clustering, the core of the system, has specific requirements that cannot be addressed by classical clustering algorithms. We emphasize the role played by the quality of the cluster labels as opposed to optimizing only the clustering structure. We highlight the main characteristics of a number of existing Web clustering engines and also discuss how to evaluate their retrieval performance. Some directions for future research are finally presented.