Flexible document organization: Comparing fuzzy and possibilistic approaches

System flexibility means the ability of a system to manage imprecise and/or uncertain information. A lot of commercially available Information Retrieval Systems (IRS) address this issue at the level of query formulation. Another way to make the flexibility of an IRS possible is by means of the flexible organization of documents. Such organization can be carried out using clustering algorithms by which documents can be automatically organized in multiple clusters simultaneously. Fuzzy and possibilistic clustering algorithms are examples of methods by which documents can belong to more than one cluster simultaneously with different membership degrees. The interpretation of these membership degrees can be used to quantify the compatibility of a document with a particular topic. The topics are represented by clusters and the clusters are identified by one or more descriptors extracted by a proposed method. We aim to investigate if the performance of each clustering algorithm can affect the extraction of meaningful overlapping cluster descriptors. Experiments were carried using well-known collections of documents and the predictive power of the descriptors extracted from both fuzzy and possibilistic document clustering was evaluated. The results prove that descriptors extracted after both fuzzy and possibilistic clustering are effective and can improve the flexible organization of documents.