Please use this identifier to cite or link to this item:
|Title:||Topic detection using MFSs|
|Source:||Yap, I.,Loh, H.T.,Shen, L.,Liu, Y. (2006). Topic detection using MFSs. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4031 LNAI : 342-352. ScholarBank@NUS Repository. https://doi.org/10.1007/11779568_38|
|Abstract:||When analyzing a document collection, a key piece of information is the number of distinct topics it contains. Document clustering has been used as a tool to facilitate the extraction of such information. However, existing clustering methods do not take into account the sequences of the words in the documents, and usually do not have the means to describe the contents within each topic cluster. In this paper, we record our investigation and results using Maximal Frequent word Sequences (MFSs) as building blocks in identifying distinct topics. The supporting documents of MFSs are grouped into an equivalence class and then linked to a topic cluster, and the MFSs serve as the document cluster identifier. We describe the original method in extracting the set of MFSs, and how it can be adapted to identify topics in a textual dataset. We also demonstrate how the MFSs themselves can act as topic descriptors for the clusters. Finally, the benchmarking study with other existing clustering methods, i.e. k-Means and EM algorithm, shows the effectiveness of our approach for topic detection. © Springer-Verlag Berlin Heidelberg 2006.|
|Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 13, 2017
checked on Dec 9, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.