Term clustering of syntactic phrases
SIGIR '90 Proceedings of the 13th annual international ACM SIGIR conference on Research and development in information retrieval
A statistical interpretation of term specificity and its application in retrieval
Document retrieval systems
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Automatic generation of overview timelines
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Vocabulary mining for information retrieval: rough sets and fuzzy sets
Information Processing and Management: an International Journal
Bursty and Hierarchical Structure in Streams
Data Mining and Knowledge Discovery
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
Topic modeling: beyond bag-of-words
ICML '06 Proceedings of the 23rd international conference on Machine learning
Detecting Temporal Trends of Technical Phrases by Using Importance Indices and Linear Regression
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Detecting Temporal Patterns of Importance Indices about Technical Phrases
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
Comparing Temporal Behavior of Phrases on Multiple Indexes with a Burst Word Detection Method
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Clustering of time series data-a survey
Pattern Recognition
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According to the accumulation of the electrically stored documents, acquisition of valuable knowledge with remarkable trends of technical terms has drawn the attentions as the topic in text mining. In order to support for discovering key topics appeared as key terms in such temporal textual datasets, we propose a method based on temporal patterns in several data-driven indices for text mining. The method consists of an automatic term extraction method in given documents, three importance indices, temporal pattern extraction by using temporal clustering, and trend detection based on linear trends of their centroids. Empirical studies show that the three importance indices are applied to the titles of two academic conferences about artificial intelligence field as the sets of documents. After extracting the temporal patterns of automatically extracted terms, we discuss the trends of the terms including the recent burst words among the titles of the conferences.