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Subspace clustering is one of the best approaches for discovering meaningful clusters in high dimensional space. However, the existing algorithms often produce clusters of great redundancy that are not easy to be understood. In this paper, based on the enumeration tree of subspace, we propose a new subspace clustering algorithm MSC to find the clusters hidden in the maximal subspace. MSC uses the...
Recently, semantic smoothing is proposed as an efficient solution for the improvement of document cluster quality. However, the existing semantic smoothing model is not effective for partitional clustering to enhance the document clustering quality. In this paper, inspired by the TF*IDF schema and background elimination strategy, we first introduce an improved semantic smoothing model, which is suitable...
In the intelligent traffic system, the research about the analysis of time series of traffic flow is important and meaningful. Using clustering methods to analyze time series not only can find some typical patterns of traffic flow, but also can group the sections of highway by their different flow characteristics. In this paper, we propose an encoded-bitmap-approach-based swap method to improve the...
Clustering is a widely used technique in data mining, at present there exists many clustering algorithms, but most existing clustering algorithms either are limited to handle the single attribute or can handle both data types but are not efficient when clustering large data sets. Few algorithms can do both well. In this article, we propose a clustering algorithm that can handle large datasets with...
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