Incremental clustering for dynamic document databases

Clustering of very large document databases is a necessity to reduce the space time complexity of retrieval operations. Continuous update of clusters is required owing to the dynamic nature of document databases. An algorithm for incremental clustering is introduced. Its complexity analysis and cost comparison with reclustering are provided. It is shown through empirical testing that the incremental clustering is as effective as the reclustering by producing clusters that are very similar. This similarity is then shown not to be by chance. In the experiments it is shown that the information retrieval effectiveness of the algorithm is compatible with the reclustering algorithm, which is known to have good retrieval performance