IEEE Transactions on Knowledge and Data Engineering
Discovering evolutionary theme patterns from text: an exploration of temporal text mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Discovering relationships among categories using misclassification information
Proceedings of the 2008 ACM symposium on Applied computing
Networked hierarchies for web directories
Proceedings of the 20th international conference companion on World wide web
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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.