The 13th International Conference on Information Integration and Web-based Applications & Services (iiWAS2011)
開催地
和文:
ホーチミン市
英文:
Ho Chi Minh City, Vietnam
アブストラクト
Our research aim is the automatic generation of a researcher'sresearch history from research articles published on the internet.Research history generation based on the k-Meansclustering algorithm has been proposed in previous work.However, the performance of the k-Means algorithm is unsatisfactory.We propose a method based on Maximum MarginClustering (MMC). MMC is a new clustering algorithmbased on Support Vector Machines (SVM). It is known thatMMC is better than existing clustering algorithms such ask-Means. In this paper, we describe how to convert articlesinto vectors using metainformation about them and how todecide an initial setting for MMC automatically. We demonstrateby experiment that the purity of a method based onMMC is about 0.58 and its entropy is about 0.415. Thisresult is better than that achieved in previous work (purity:0.35, entropy: 0.47).
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