Learning element similarity matrix for semi-structured document analysis

Yang, Jianwu; Cheung, William K.; Chen, Xiaoou

doi:10.1007/s10115-008-0138-2

Cited by 18 publications

(10 citation statements)

References 16 publications

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“…x and doc y is defined as [2] ( , ) M for a specific type of XML data, SLVM-based document similarity learn the matrix using pair-wise similar training data (unsupervised learning) in an iterative manner [4].…”

Section: B Similarity Measuresmentioning

confidence: 99%

Lexical-semantic SLVM for XML Document Classification

Long

Wang

et al. 2014

JSW

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Structured link vector model (SLVM) and its improved version depend on statistical term measures to implement XML document representation. As a result, they ignore the lexical semantics of terms and its mutual information, leading to text classification errors. This paper proposed a XML document representation method, WordNet-based lexical-semantic SLVM, to solve the problem. Using WordNet, this method constructed a data structure for characterizing lexical semantic contents of XML document, and adjusted EM modeling to disambiguate word stems. Then, synset matrix of lexical semantic contents was built in the lexical-semantic feature space for XML document representation, and lexical semantic relations were marked on it to construct the feature matrix in lexical-semantic SLVM. On categorized dataset of Wikipedia XML, using NWKNN classification algorithm, the experimental results show that the feature matrix of our method performs F1 measure better than original SLVM and frequent sub-tree SLVM based on TF-IDF.

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Section: B Similarity Measuresmentioning

confidence: 99%

Lexical-semantic SLVM for XML Document Classification

Long

Wang

et al. 2014

JSW

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“…Clustering [6,9,16,23,30,32,34], which aims to organize data in an unsupervised fashion, is one of the fundamental problems in data mining and machine learning. The basic goal is to group the data points into clusters such that the data in the same cluster are "similar" to each other, while the data in different clusters are "different" from each other.…”

Section: Introductionmentioning

confidence: 99%

Improving clustering by learning a bi-stochastic data similarity matrix

Wang

König

et al. 2011

Knowl Inf Syst

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An idealized clustering algorithm seeks to learn a cluster-adjacency matrix such that, if two data points belong to the same cluster, the corresponding entry would be 1; otherwise, the entry would be 0. This integer (1/0) constraint makes it difficult to find the optimal solution. We propose a relaxation on the cluster-adjacency matrix, by deriving a bi-stochastic matrix from a data similarity (e.g., kernel) matrix according to the Bregman divergence. Our general method is named the Bregmanian Bi-Stochastication (BBS) algorithm. We focus on two popular choices of the Bregman divergence: the Euclidean distance and the Kullback-Leibler (KL) divergence. Interestingly, the BBS algorithm using the KL divergence is equivalent to the Sinkhorn-Knopp (SK) algorithm for deriving bi-stochastic matrices. We show that the BBS algorithm using the Euclidean distance is closely related to the relaxed k-means clustering and can often produce noticeably superior clustering results to the SK algorithm (and other algorithms such as Normalized Cut), through extensive experiments on public data sets.

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“…In short, the continuous growth of the XML documents has raised a number of issues concerning the way to manage these documents, such as retrieving, indexing [9,24,25,29,31,35,38,39,47,50], and/or clustering [4,13,16,18,20,21,[32][33][34]36], these documents.…”

Section: Introductionmentioning

confidence: 99%

Using structural similarity for clustering XML documents

et al. 2011

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In this paper, we describe a method for clustering XML documents. Its goal is to group documents sharing similar structures. Our approach is two-step. We first automatically extract the structure from each XML document to be classified. This extracted structure is then used as a representation model to classify the corresponding XML document. The idea behind the clustering is that if XML documents share similar structures, they are more likely to correspond to the structural part of the same query. Finally, for the experimentation purpose, we tested our algorithms on both real (ACM SIGMOD Record corpus) and synthetic data. The results clearly demonstrate the interest of our approach.

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Learning element similarity matrix for semi-structured document analysis

Cited by 18 publications

References 16 publications

Lexical-semantic SLVM for XML Document Classification

Lexical-semantic SLVM for XML Document Classification

Improving clustering by learning a bi-stochastic data similarity matrix

Using structural similarity for clustering XML documents

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