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An effective XML cluster method called neighbor center clustering algorithm (NCC) is presented in this paper, whose similarity is obtained through both structural and content information contained in XML files. Structural similarity is measured by the idea of Longest Common Subsequence, while content similarity is achieved using TF-IDF principles. It reduces computation complexity by avoiding direct...
Efficient data mining and indexing is important for multimedia analysis and retrieval. In the field of large-scale video analysis, effective genre categorization plays an important role and serves one of the fundamental steps for data mining. Existing works utilize domain-knowledge dependent feature extraction, which is limited from genre diversification as well as data volume scalability. In this...
K-Nearest-Neighbor (KNN) as an important classification method based on closest training examples has been widely used in data mining due to its simplicity, effectiveness, and robustness. However, the class probability estimation, the neighborhood size and the type of distance function confronting KNN may affect its classification accuracy. Many researchers have been focused on improving the accuracy...
In high dimensional data space, clusters are likely to exist in different subspaces. K-means is a classic clustering algorithm, but it cannot be used to find subspace clusters. In this paper, an algorithm called GKM is designed to generalize k-means algorithm for high dimensional data. In the objective function of GKM, we associate a weight vector with each cluster to indicate which dimensions are...
Many machine learning algorithms can be applied only to data described by categorical attributes. So discretization of continuous attributes is one of the important steps in preprocessing of extracting knowledge. Traditional discretization algorithms based on clustering need a pre-determined clustering number k, also typically are applied in an unsupervised learning framework. This paper describes...
Due to homonyms, abbreviations, etc., name ambiguity is widely available in Web and e-document. For example, when integrating heterogeneous literature databases, because there are different name specifications, different authors may be thought of as the same author, and vice versa. Therefore, name ambiguity makes data robust even dirty and lowers the precision of information retrieval. In this paper,...
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