Incremental Spectral Clustering With Application to Monitoring of Evolving Blog Communities

Abstract

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2007 SIAM International Conference on Data Mining (SDM)Incremental Spectral Clustering With Application to Monitoring of Evolving Blog CommunitiesHuazhong Ning, Wei Xu, Yun Chi, Yihong Gong, and Thomas HuangHuazhong Ning, Wei Xu, Yun Chi, Yihong Gong, and Thomas Huangpp.261 - 272Chapter DOI:https://doi.org/10.1137/1.9781611972771.24PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract In recent years, spectral clustering method has gained attentions because of its superior performance compared to other traditional clustering algorithms such as K-means algorithm. The existing spectral clustering algorithms are all off-line algorithms, i.e., they can not incrementally update the clustering result given a small change of the data set. However, the capability of incrementally updating is essential to some applications such as real time monitoring of the evolving communities of websphere or blogsphere. Unlike traditional stream data, these applications require incremental algorithms to handle not only insertion/deletion of data points but also similarity changes between existing items. This paper extends the standard spectral clustering to such evolving data by introducing the incidence vector/matrix to represent two kinds of dynamics in the same framework and by incrementally updating the eigenvalue system. Our incremental algorithm, initialized by a standard spectral clustering, continuously and efficiently updates the eigenvalue system and generates instant cluster labels, as the data set is evolving. The algorithm is applied to a blog data set. Compared with recomputation of the solution by standard spectral clustering, it achieves similar accuracy but with much lower computational cost. Close inspection into the blog content shows that the incremental approach can discover not only the stable blog communities but also the evolution of the individual multi-topic blogs. Previous chapter Next chapter RelatedDetails Published:2007ISBN:978-0-89871-630-6eISBN:978-1-61197-277-1 https://doi.org/10.1137/1.9781611972771Book Series Name:ProceedingsBook Code:PR127Book Pages:xiv + 648Key words:Incremental clustering, Spectral Clustering, Incidence Vector/Matrix, Web-blogs