Self-organizing maps
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
An analysis of the relative hardness of Reuters-21578 subsets: Research Articles
Journal of the American Society for Information Science and Technology
Semi-supervised single-label text categorization using centroid-based classifiers
Proceedings of the 2007 ACM symposium on Applied computing
Self organization of a massive document collection
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
In this paper we present a method to cluster documents taking into account short contextual information within the document, further which is weighted by importance and used as input to self organizing map. A knowledge network is a knowledge map which clusters similar knowledge sources into knowledge domain. Knowledge sources taken as input and created as a conceptual map of the domain space. Like sources are clustered and are placed together on the map. This work tries to evaluate the value of Kohonen self organizing map i.e. topology-preserving map for use as an interactive textual knowledge mapping tool for categorizing set of textual knowledge sources. This work develops a method for clustering a set of documents related to foundry industry. Based on textual similarity, collection of huge documents is organized into clusters. The work provides an interactive and user-friendly system for foundry industry where collection of documents is mapped into clusters based on the context of the text. To achieve the mapping of knowledge sources to clusters, a neural network approach for clustering the documents is used. This is based on Kohenon's self-organizing maps (SOM) i.e. topology-preserving map which is an unsupervised competitive method of learning.