Enhancing text clustering model based on truncated singular value decomposition, fuzzy art and cross validation

Djellali, Choukri

doi:10.1145/2492517.2500317

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…It has been shown that by modification of the original Fuzzy ART neural network there can be reached the excellent results in the area of clustering and categorization of text documents [5], [7], [28]- [30]. One of the above described modifications, which enables fuzzy clustering is a KMART network [5].…”

Section: Proposal Of a Modified Model Of Kmart Network For Fuzzy Clus...mentioning

confidence: 99%

The Category Proliferation Problem in ART Neural Networks

Marček¹,

Rojček²

2017

ACTA POLYTECH HUNG

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This article describes the design of a new model IKMART, for classification of documents and their incorporation into categories based on the KMART architecture. The architecture consists of two networks that mutually cooperate through the interconnection of weights and the output matrix of the coded documents. The architecture retains required network features such as incremental learning without the need of descriptive and input/output fuzzy data, learning acceleration and classification of documents and a minimal number of user-defined parameters. The conducted experiments with real documents showed a more precise categorization of documents and higher classification performance in comparison to the classic KMART algorithm.

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Section: Proposal Of a Modified Model Of Kmart Network For Fuzzy Clus...mentioning

confidence: 99%

The Category Proliferation Problem in ART Neural Networks

Marček¹,

Rojček²

2017

ACTA POLYTECH HUNG

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“…The TSVD process calculates an approximation of lower rank that takes advantage of the correlation of terms as shown in Figure ( In order to automatically identify the number of clusters, we used a clustering model based on the Fuzzy Adaptive Resonance Theory [9], [13]. This dynamic model allows the neural network to automatically adjust its size depending on the dynamics of the environment (dynamic knowledge, complex shapes, variables distributions, incremental acquisition, etc) (stability-plasticity dilemma).…”

Section: Variable Selectionmentioning

confidence: 99%

“…This criterion is defined as follows : The convergence speed of our clustering model is based on typical initializations [13]. This initialization scheme reduces the computation time and improves the convergence speed to achieve the neighbourhood vicinity of the response.…”

Section: Clusteringmentioning

confidence: 99%

A Semantic-based Variables Selection for Ontology Learning Taking Jaccard Alignment as Case

Djellali¹

2014

Procedia Computer Science

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In the past decade, research on numerical schemes on ontology learning has been quite intensive. Several learning approaches have been proposed to help developers during the maintenance process. Most of the proposed approaches do not process the curse of dimensionality and the semantic contained in the information structure. A novel semantic-based method for ontology learning, which can provide improvement in both alignment and learning, is described. Good comparisons with the experimental studies demonstrate the multidisciplinary applications of our approach.

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