Extracting meaningful labels for WEBSOM text archives
Proceedings of the tenth international conference on Information and knowledge management
Evaluating Keyword Selection Methods for WEBSOM Text Archives
IEEE Transactions on Knowledge and Data Engineering
Semantic mapping and K-means applied to hybrid SOM-based document organization system construction
Proceedings of the 2008 ACM symposium on Applied computing
| Hi-index | 0.00 |
Abstract: Not only have Self-Organizing Maps (SOMs), such as the WEBSOM, been shown to scale up to very large datasets, these maps also allow for a novel mode of navigating through a large collection of text documents. The entire text collection is presented to a user as a regular map, where each point in the map is associated to a group of documents that are likely to be composed of similar terms and phrases. In addition, the closer two points are in the map, the more similar are their respective associated documents. Thus, once an interesting document is found in the map, the user just has to click around the vicinity of that document to retrieve other similar documents. A major drawback of SOMs, however, is the long training time required, especially for document collections where both the volume and the dimensionality are huge. In this paper, we demonstrate how the size of the initial text collection is progressively and drastically reduced from the raw document collection to the final SOM-based text archive. We demonstrate this using a widely studied Reuters collection.