Improving k-means through distributed scalable metaheuristics

Oliveira, Gilberto Viana de; Coutinho, F.P.; Campello, Ricardo J. G. B.; Naldi, Murilo Coelho

doi:10.1016/j.neucom.2016.07.074

Cited by 24 publications

(13 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To overcome the limitations, we will use the Elbow scheme [35] that can find a proper number of clusters. Also, we will use the Silhouette scheme [36,37] to validate the performance of clustering results by K-means clustering scheme. The detailed descriptions of the two schemes will be provided in next section with performance evaluation.…”

Section: K-means Clusteringmentioning

confidence: 99%

“…The silhouette scheme is one of various evaluation methods as a measure to evaluate the performance of clustering [36,37]. The silhouette value becomes higher as two data within a same cluster is closer.…”

Section: Applying Silhouette Schemementioning

confidence: 99%

“…Typically, a silhouette value ranges from − 1 to 1, where a high value indicates that data are well matched to their own cluster and poorly matched to neighboring clusters. Generally, the silhouette value more than 0.5 means that clustering results are validated [36,37]. Table 5 shows an average silhouette value for each of the three methods shown in Table 3.…”

Section: Applying Silhouette Schemementioning

confidence: 99%

See 2 more Smart Citations

Research paper classification systems based on TF-IDF and LDA schemes

Kim

Gil

2019

Hum. Cent. Comput. Inf. Sci.

229

View full text Add to dashboard Cite

With the increasing advance of computer and information technologies, numerous research papers have been published online as well as offline, and as new research fields have been continuingly created, users have a lot of trouble in finding and categorizing their interesting research papers. In order to overcome the limitations, this paper proposes a research paper classification system that can cluster research papers into the meaningful class in which papers are very likely to have similar subjects. The proposed system extracts representative keywords from the abstracts of each paper and topics by Latent Dirichlet allocation (LDA) scheme. Then, the K-means clustering algorithm is applied to classify the whole papers into research papers with similar subjects, based on the Term frequency-inverse document frequency (TF-IDF) values of each paper.

show abstract

Section: K-means Clusteringmentioning

confidence: 99%

Section: Applying Silhouette Schemementioning

confidence: 99%

Section: Applying Silhouette Schemementioning

confidence: 99%

See 1 more Smart Citation

Research paper classification systems based on TF-IDF and LDA schemes

Kim

Gil

2019

Hum. Cent. Comput. Inf. Sci.

229

View full text Add to dashboard Cite

show abstract

“…Subgroup discovery (SD) is also addressed in this period by means of GP applied to the derivation of fuzzy rules (Carmona et al, ) in a medicine‐specific application. Finally, clustering is performed on big data problems by means of k‐ means‐based ensembles optimized by a GA within an MR framework (Oliveira et al, ).…”

Section: Most Cited Papers On Edm (2007–2017)mentioning

confidence: 99%

“…Subgroup discovery (SD) is also addressed in this period by means of GP applied to the derivation of fuzzy rules (Carmona et al, 2015) in a medicine-specific application. Finally, clustering is performed on big data problems by means of k-means-based ensembles optimized by a GA within an MR framework (Oliveira et al, 2017). (2007-2011 and 2012-2017) The ability of EAs to manage a set of solutions, even attending to multiple objectives, as well as their ability to optimize any kinds of values, allows them to be successfully applied in a wide variety of applications.…”

Section: Unsupervised (Second Period)mentioning

confidence: 99%

Evolutionary data mining and applications: A revision on the most cited papers from the last 10 years (2007–2017)

Alcalá

Gacto

Alcalá‐Fdez

2017

WIREs Data Min & Knowl

View full text Add to dashboard Cite

The ability of evolutionary algorithms (EAs) to manage a set of solutions, even attending multiple objectives, as well as their ability to optimize any kinds of values, allows them to fit very well some parts of the data‐mining (DM) problems, whose native learning techniques usually associated with the inherent DM problem are not able to solve. Therefore, EAs are widely applied to complement or even replace the classical DM learning approaches. This application of EAs to the DM process is usually named evolutionary data mining (EDM). This contribution aims at showing a glimpse of the EDM field current state by focusing on the most cited papers published in the last 10 years. A descriptive analysis of the papers together with a bibliographic study is performed in order to differentiate past and current trends and to easily focus on significant further developments. Results show that, in the case of the most cited studied papers, the use of EAs on DM tasks is mainly focused on enhancing the classical learning techniques, thus completely replacing them only when it is directly motivated by the nature of problem. The bibliographic analysis is also showing that even though EAs were the main techniques used for EDM, the emergent evolutionary computation algorithms (swarm intelligence, etc.) are becoming nowadays the most cited and used ones. Based on all these facts, some potential further directions are also discussed. WIREs Data Mining Knowl Discov 2018, 8:e1239. doi: 10.1002/widm.1239 This article is categorized under: Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Computational Intelligence Technologies > Classification Technologies > Prediction

show abstract

Differential Privacy Protection Algorithm for Data Clustering Center

Yang

Liu

2023

Machine Learning for Cyber Security

View full text Add to dashboard Cite

Improving k-means through distributed scalable metaheuristics

Cited by 24 publications

References 24 publications

Research paper classification systems based on TF-IDF and LDA schemes

Research paper classification systems based on TF-IDF and LDA schemes

Evolutionary data mining and applications: A revision on the most cited papers from the last 10 years (2007–2017)

Differential Privacy Protection Algorithm for Data Clustering Center

Contact Info

Product

Resources

About