Abstract
Domain-Specific features clustering aims to cluster the features from related domains into K clusters. Although traditional clustering algorithms can be used to domain-specific features clustering, the performance may not good as the features have little inter-connection in related domains. In this paper, we develop a solution that uses the domain-independent feature as a bridge to connect the domain-specific features. And we use spectral clustering to cluster the domain-specific features into K clusters. We present theoretical analysis to show that our algorithm is able to produce high quality clusters. The experimental results show that our algorithm improves the clustering performance over the traditional algorithms.
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Yang, X., Wang, M., Fang, L., Yue, L., Lv, Y. (2012). Research on Domain-Specific Features Clustering Based Spectral Clustering. In: Tan, Y., Shi, Y., Ji, Z. (eds) Advances in Swarm Intelligence. ICSI 2012. Lecture Notes in Computer Science, vol 7332. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31020-1_11
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DOI: https://doi.org/10.1007/978-3-642-31020-1_11
Publisher Name: Springer, Berlin, Heidelberg
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