Soft DBSCAN: Improving DBSCAN clustering method using fuzzy set theory

Smiti, Abir; Eloudi, Zied

doi:10.1109/hsi.2013.6577851

Cited by 46 publications

(25 citation statements)

References 11 publications

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“…Smiti and Elouedi compared DBSCAN-GM with K-means, G-means, and DBSCAN [8,9]. They measured the performance of the algorithms using PCC and the validity index above.…”

Section: Aliditymentioning

confidence: 99%

“…The DBSCAN-GM and VDBSCAN (varied density-based spatial clustering of applications with noise) algorithms were produced for obtaining good clustering without input parameters [7,8]. Fuzzy neighborhood DBSCAN (FN-DBSCAN) and soft-DBSCAN algorithms were produced for robustness [9,10]. Fast clustering, active-DBSCAN, and FDBSCAN algorithms were developed [11,12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On parameter adjustment of the fuzzy neighborhood-based clustering algorithms

Çıklaçandır¹,

Ulutagay²,

Utku³

et al. 2019

Turk J Elec Eng & Comp Sci

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Day by day huge amounts data are produced, and evaluation of these data becomes more difficult. The data obtained should provide meaningful, correct, and accurate information. Therefore, all data must be separated into clusters correctly, and the right information from these clusters must be obtained. Having the correct clusters depends on the clustering algorithm that is used. There are many clustering algorithms. The density-based methods are very important among the groups of clustering methods, as they can find arbitrary shapes. An advanced model of the density-based spatial clustering of applications with noise (DBSCAN) algorithm, called fuzzy neighborhood DBSCAN Gaussian means (FN-DBSCAN-GM), is offered in this study. The main contribution of FN-DBSCAN-GM is to find the parameters automatically and to divide the data into clusters robustly. The effectiveness of FN-DBSCAN-GM has been demonstrated on overlapping datasets (six artificial and two real-life datasets). The performances of these datasets are compared with the percentage of correct classification and validity index. Our experiments showed that this new algorithm was a preferable and robust algorithm.

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“…Smiti and Elouedi compared DBSCAN-GM with K-means, G-means, and DBSCAN [8,9]. They measured the performance of the algorithms using PCC and the validity index above.…”

Section: Aliditymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On parameter adjustment of the fuzzy neighborhood-based clustering algorithms

Çıklaçandır¹,

Ulutagay²,

Utku³

et al. 2019

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…There are many clustering methods including K-means [39], nearest neighbor clustering [40], fuzzy clustering [41], DBSCAN [42], etc. In the MTSVR model, clustering granulation is performed to examine the rate of data distribution.…”

Section: Clusteringmentioning

confidence: 99%

Modified twin support vector regression

2016

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“…La implementación de características de n-gramas sintácticos en el texto [36,37] o distintos tipos de n-gramas [17,38,39] en la etapa de preselección, puede ayudar a tener una mejor generación de fragmentos para formar pasajes plagiados. La implementación de diversos agrupadores en la etapa de extensión en combinación con la etapa de pre-selección, es algo que a lo largo de las competiciones de PAN 2012 y PAN 2014 se ha presentado, y también es una opción implementar y analizar algunos agrupadores [40,41], Si bien ya se han registrado resultados favorables, la experimentación con nuevas heurísticas, nos abrirá el panorama, en cuanto la utilidad de diferentes técnicas tanto para resolver este problema como algunos otros que se relacionen. Tippman, M., Barrón-Cedeño, A., Gupta, P., Rosso, P., Stein, B.: Overview of the 4th International competition on Plagiarism Detection.…”

Section: Conclusionesunclassified

Efecto del pre-procesamiento en la detección automática de plagio para PAN 2014 y PAN 2015

García¹,

Ledeneva²,

García-Hernández³

2016

RCS

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Dentro de la detección automática de plagio, el alineamiento de texto en [1] lo define como el descubrimiento de fragmentos similares de texto entre dos documentos. La cual puede utilizarse en: detección de plagio, identificación de autoría, detección de reúso de texto, recuperación de información, entre muchas otras. El pre-procesamiento consta de diversas técnicas que se aplica en la mayoría de las tareas del Procesamiento del Lenguaje Natural (PLN), en este caso, las heurísticas presentadas son tomadas de los trabajos [1] y [2] de las mejores participaciones en la competencia internacional de detección automática de plagio PAN 2014 y PAN 2015 en la sub-tarea alineamiento de texto monolingüe, con la finalidad de conocer el efecto que tiene la eliminación de stopwords y el uso o no de stemming en las heurísticas antes mencionadas, que son técnicas dentro del pre-procesamiento.

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Soft DBSCAN: Improving DBSCAN clustering method using fuzzy set theory

Cited by 46 publications

References 11 publications

On parameter adjustment of the fuzzy neighborhood-based clustering algorithms

On parameter adjustment of the fuzzy neighborhood-based clustering algorithms

Modified twin support vector regression

Efecto del pre-procesamiento en la detección automática de plagio para PAN 2014 y PAN 2015

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