Mining temporal relationships among categories

Temporal text mining deals with discovering temporal patterns in text over a period of time. A Theme Evolution Graph (TEG) is used to visualize when new themes are created and how they evolve with respect to time. TEG, however, does not represent relationships among themes (or categories) that share same timestamp. We focus on identifying such relationships and represent them in Relationship Evolution Graph (REG). We favorably compare passage misclassification and association rule mining with three existing approaches, namely KL divergence (KLD), Consistent bipartite spectral co-partitioning graph (CBSCG) and document misclassification. Our evaluations indicate that association rule mining approach statistically significantly (99% confidence) outperforms the other existing approaches, while passage misclassification approach is the second most effective approach. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval] Clustering General Terms Algorithms, Experimen...

• Type: Conference
• Year: 2010
• Where: SAC
• Authors: Saket S. R. Mengle, Nazli Goharian
• Keywords: Applied Computing, Association Rule Mining, Evolution Graph, SAC 2010, Temporal Text Mining