Similarity-Based Text Clustering: A Comparative Study

Ghosh, Joydeep; Strehl, Alexander L.

doi:10.1007/3-540-28349-8_3

Cited by 20 publications

(15 citation statements)

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“…The interpretation of the resulting values are similar to the cosine similarity measure: a value of 0 means that the two documents are entirely dissimilar, while a value of 1 means the opposite. Compared to the original cosine dissimilarity measure [9], these variants are less suited in a document clustering environment.…”

Section: Cosine Dissimilarity Measure Variantsmentioning

confidence: 97%

Pairwise-adaptive dissimilarity measure for document clustering

D’hondt

Vertommen

Verhaegen

et al. 2010

Information Sciences

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Section: Cosine Dissimilarity Measure Variantsmentioning

confidence: 97%

Pairwise-adaptive dissimilarity measure for document clustering

D’hondt

Vertommen

Verhaegen

et al. 2010

Information Sciences

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“…This work considers the extended Jaccard distance which measure dissimilarity between two sample sets, and is complementary to the Jaccard coefficient [36] which is a variant of normalized inner product. Each mean vector of two motions in comparison is represented by (14) using a specific variational parameter γ inferred from each specific motion data in the LNS space.…”

Section: Strategy Analysismentioning

confidence: 99%

Supervised Hierarchical Bayesian Model-Based Electomyographic Control and Analysis

Han

2014

IEEE J. Biomed. Health Inform.

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This work suggests a supervised hierarchical Bayesian model for surface electromyography (sEMG)-based motion classification and its strategy analysis. The proposed model unifies the optimal feature extraction and classification through probabilistic inference and learning by identifying the latent neural states (LNSs) that govern a collection of sEMG signals. In addition, the inference step provides an approach to identify distinct muscle activation strategies according to sEMG patterns based on LNSs. To validate the model, nine-class classification using four sEMG sensors on the limb motions is tested. The model performance is evaluated with relatively high and low activation levels, generalized classification across subjects and online classification. The model, based on LNSs to capture various motions, is assessed with respect to activation levels, individual subjects and transition during online classification. Our approach cannot only classify sEMG patterns, but also provide the interpretation of sEMG strategic patterns. This work supports the potential of the proposed model for sEMG control-based applications.

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“…In the VSM, there are several alternatives to quantify the semantic similarity between document pairs. Among them, Cosine similarity has shown to be an effective measure [11], and for a pair of document vectors, d i and d j is given by…”

Section: The Bag Of Words Modelmentioning

confidence: 99%

Text document clustering using global term context vectors

Kalogeratos

Likas

2011

Knowl Inf Syst

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Despite the advantages of the traditional vector space model (VSM) representation, there are known deficiencies concerning the term independence assumption. The high dimensionality and sparsity of the text feature space and phenomena such as polysemy and synonymy can only be handled if a way is provided to measure term similarity. Many approaches have been proposed that map document vectors onto a new feature space where learning algorithms can achieve better solutions. This paper presents the global term context vector-VSM (GTCV-VSM) method for text document representation. It is an extension to VSM that: (i) it captures local contextual information for each term occurrence in the term sequences of documents; (ii) the local contexts for the occurrences of a term are combined to define the global context of that term; (iii) using the global context of all terms a proper semantic matrix is constructed; (iv) this matrix is further used to linearly map traditional VSM (Bag of Words-BOW) document vectors onto a 'semantically smoothed' feature space where problems such as text document clustering can be solved more efficiently. We present an experimental study demonstrating the improvement of clustering results when the proposed GTCV-VSM representation is used compared with traditional VSM-based approaches.

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Similarity-Based Text Clustering: A Comparative Study

Cited by 20 publications

References 0 publications

Pairwise-adaptive dissimilarity measure for document clustering

Pairwise-adaptive dissimilarity measure for document clustering

Supervised Hierarchical Bayesian Model-Based Electomyographic Control and Analysis

Text document clustering using global term context vectors

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