Keen-Means: A Web Page Clustering Tool Based on an Self-Adjustable K-Means Algorithm

Chun-Hsiung Tseng, Yung-Hui Chen, C. Chuang, Jia Hua Wu, Yi Syuan Yang, Yaning Liang
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引用次数: 3

Abstract

Search engines usually do their jobs well. However, due to the fact that most existing search algorithms are keyword-based, search engines may not work as expected in some scenarios when ambiguity problems are encountered. A possible approach to overcome it is to categorize Web resources in advance. In this research, a k-means variation, the keen-means algorithm, along with its implementation is proposed. The algorithm will dynamically and automatically adjust the k value to achieve better results.
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基于自调节K-Means算法的网页聚类工具
搜索引擎通常会做得很好。然而,由于现有的大多数搜索算法都是基于关键字的,在某些情况下,当遇到歧义问题时,搜索引擎可能无法达到预期的效果。一种可能的解决方法是提前对Web资源进行分类。在本研究中,提出了一种k-means变化,即keen-means算法及其实现。该算法会动态自动调整k值以达到更好的效果。
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