Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Recent trends in hierarchic document clustering: a critical review
Information Processing and Management: an International Journal
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Reexamining the cluster hypothesis: scatter/gather on retrieval results
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating document clustering for interactive information retrieval
Proceedings of the tenth international conference on Information and knowledge management
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Modern Information Retrieval
ICSC '99 Proceedings of the 5th International Computer Science Conference on Internet Applications
Query-sensitive similarity measures for information retrieval
Knowledge and Information Systems
Web searching on the Vivisimo search engine
Journal of the American Society for Information Science and Technology
Introduction to Information Retrieval
Introduction to Information Retrieval
Genetic algorithm based multi-document summarization
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
An Evolutionary Approach to Multiobjective Clustering
IEEE Transactions on Evolutionary Computation
Hi-index | 0.00 |
Document clustering techniques have been widely applied in Information Retrieval to reorganize results furnished as a response to user's queries. Following the Cluster Hypothesis which states that relevant documents tend to be more similar one to each other than to non-relevant ones, most of relevant documents are likely to be gathered in a single cluster. Usually, systems organizing search results as a set of clusters consider this tendency as a very advantageous phenomenon, since it allows to filter the results provided by the initial search. Adopting a different point of view, we rather consider the Cluster Hypothesis as a hindrance to the information access since it prevents the emergence of the various aspects of the query. The risk induced is to restrict the perception of the subject to an unique point of view. Therefore, we propose to rather distribute the relevant documents over clusters by orienting the organization of the clusters according to the user's topic. The aim is to attract the clusters around the latter in order to highlight the thematic differences between documents which are strongly connected to the query. Rather than modifying the inter-documents similarity computation as it is the case in several studies, we propose to directly act on the organization of the clusters by using a multi-objective evolutionary clustering algorithm which, besides the classical internal cohesion, also optimizes the query proximity of the clusters. First experimental results highlight the great benefit which may be gained by our way of query consideration.