Unsupervised Hierarchical CLustering Based on Sequential Partitioning and Merging

Sheng-Jyh Wang*

*此作品的通信作者

研究成果: Conference contribution同行評審

摘要

In this talk, we present an unsupervised hierarchical clustering method based on the split-and-merge scheme. In the splitting phase, we sequentially partition the feature space of the given data into smaller cells so that the probability distribution of the feature points within each cell follows a Gaussian function. In the merging phase, we sequentially merge cells of similar feature property into larger cells to construct a hierarchical description of the data. This hierarchical representation is very efficient and effective in describing the inherent structure of the data, especially data of high dimension. An application of this unsupervised hierarchical clustering algorithm in image segmentation is also presented to demonstrate the feasibility of this new approach.
原文English
主出版物標題2016 INTERNATIONAL SYMPOSIUM ON VLSI DESIGN, AUTOMATION AND TEST (VLSI-DAT)
發行者IEEE
頁數1
ISBN(電子)978-1-4673-9498-7
出版狀態Published - 2 六月 2016
事件International Symposium on VLSI Design, Automation and Test (VLSI-DAT) - Hsinchu
持續時間: 25 四月 201627 四月 2016

Conference

ConferenceInternational Symposium on VLSI Design, Automation and Test (VLSI-DAT)
城市Hsinchu
期間25/04/1627/04/16

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