Clustering of time series data-a survey
Pattern Recognition
A social network-empowered research analytics framework for project selection
Decision Support Systems
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In order to detect research keys in academic researches, we propose a method based on temporal patterns of technical terms by using several data-driven indices and their temporal clusters. In text mining framework, data-driven indices are used as importance indices of words and phrases. Although the values of these indices are influenced by usages of terms, conventional emergent term detection methods did not treat these indices explicitly. Our method consists of an automatic term extraction method in given documents, three importance indices from text mining studies, and temporal patterns based on results of temporal clustering. Then, we assign abstracted sense of the temporal patterns of the terms based on their linear trends of centroids. Empirical studies show that an importance index is applied to the titles of four annual conferences about data mining field as sets of documents. After extracting the temporal patterns of automatically extracted terms, we compared the linear trends of the technical terms among the titles of one conference.