System for fuzzy document clustering and fast fuzzy classification

Rojček, Michal

doi:10.1109/cinti.2014.7028711

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…Unlike experiments with synthetic documents[32], this did not create any new category (which is correct) but both F-measures were lower than in the Fuzzy categorization algorithm with the weight adaptation and equal to or lower than in the Fuzzy categorization algorithm without the weight adaptation. It is caused by the incorrect document categorization, what is also shown in Figure3.…”

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confidence: 92%

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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The Category Proliferation Problem in ART Neural Networks

Marček¹,

Rojček²

2017

ACTA POLYTECH HUNG

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“…The judgment of text feature selection quality through the use of information retrieval techniques indicates the third measure.. The relation-ship between the features and the measures are identified through the use of linear mixed-effects regression models [1]. TREC document analysis is essential to developers especially in large-scale text analysis systems because they are used to fix feature extraction based document classification.…”

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confidence: 99%

An novel cluster based feature selection and document classification model on high dimension trec data

Kumari¹,

Satyanarayana²

2017

IJET

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TREC text documents are complex to analyze the features its relevant similar documents using the traditional document similarity measures. As the size of the TREC repository is increasing, finding relevant clustered documents from a large collection of unstructured documents is a challenging task. Traditional document similarity and classification models are implemented on homogeneous TREC data to find essential features for document entities that are similar to the TREC documents. Also, most of the traditional models are applicable to limited text document sets for text analysis. The main issues in the traditional text mining models in TREC repository include :1) Each document is represented in vector form with many sparsity values 2) Failed to find the document semantic similarity between the intra and inter clusters 3) High mean squared error rate. In this paper, novel feature selection based clustered and classification model is proposed on large number of different TREC repositories. Traditional latent Semantic Indexing and document clustering models are failed to find the topic relevance on large number of TREC clinical text document sets due to computational memory and time. Proposed document feature selection and clustered based classification model is applied on TREC clinical benchmark datasets. From the experimental results, it is proved that the proposed model is efficient than the existing models in terms of computational memory, accuracy and error rate are concerned.

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Distributed Big Data Clustering using MapReduce-based Fuzzy C-Medoids

Sardar

Ansari

2021

J. Inst. Eng. India Ser. B

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System for fuzzy document clustering and fast fuzzy classification

Cited by 5 publications

References 13 publications

The Category Proliferation Problem in ART Neural Networks

The Category Proliferation Problem in ART Neural Networks

An novel cluster based feature selection and document classification model on high dimension trec data

Distributed Big Data Clustering using MapReduce-based Fuzzy C-Medoids

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