@inproceedings{28c840fc2f5b460e824ff161558fd392,
title = "Identifying evolutionary topic temporal patterns based on bursty phrase clustering",
abstract = "We discuss a temporal text mining task on finding evolutionary patterns of topics from a collection of article revisions. To reveal the evolution of topics, we propose a novel method for finding key phrases that are bursty and significant in terms of revision histories. Then we show a time series clustering method to group phrases that have similar burst histories, where additions and deletions are separately considered, and time series is abstracted by burst detection. In clustering, we use dynamic time warping to measure the distance between time sequences of phrase frequencies. Experimental results show that our method clusters phrases into groups that actually share similar bursts which can be explained by real-world events.",
keywords = "Burst detection, Clustering, DTW, Temporal pattern, Topic evolution",
author = "Yixuan Liu and Zihao Gao and Mizuho Iwaihara",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 1st Asia-Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2017 ; Conference date: 07-07-2017 Through 09-07-2017",
year = "2017",
doi = "10.1007/978-3-319-63564-4_22",
language = "English",
isbn = "9783319635637",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "276--284",
editor = "Jensen, {Christian S.} and Xiang Lian and Lei Chen and Cyrus Shahabi and Xiaochun Yang",
booktitle = "Web and Big Data - 1st International Joint Conference, APWeb-WAIM 2017, Proceedings",
}