A High-Order CFS Algorithm for Clustering Big Data

Bu, Fanyu; Chen, Zhikui; Tang, Tong; Zhang, Ying

doi:10.1155/2016/4356127

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…In MDC-BHS technique, Clustering accuracy is measured as ratio of number of data that are correctly clustered to the total number of data taken as input. The clustering accuracy is determined in terms of percentage (%) and mathematically formulated as, (9) From equation (9), clustering accuracy of big data is evaluated. When clustering accuracy is higher, the MDC-BHS technique is said to be more effectual.…”

Section: A Performance Results Of Clustering Accuracymentioning

confidence: 99%

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Moore Data Clustering Based Bloom Hash Storage for Dimensionality Reduction of Big Data Analytics

K*¹,

Maheswari.²

2019

IJRTE

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Big data contains massive amounts of information’s that are difficult to manage, acquire, store and analyses. The clustering of data is a demanding issue in the field of big data analytics. The existing techniques developed for clustering does not provide efficient performance and also time complexity of clustering was higher. Further, minimizing dimensionality of big data was not addressed effectively. In order to overcome these limitations, a Moore Data Clustering based Bloom Hash Storage (MDC-BHS) Technique is proposed. The MDC-BHS Technique is designed with aim of reducing the dimensionality of big data with lesser time through clustering. The MDC-BHS Technique used Moore Data Clustering (MDC) Model in order to group the data in big dataset with minimum time consumption. After performing clustering process, the MDC-BHS Technique employed Bloom Hash Storage (BHS) Model in order to store clustered data with minimum space complexity. The BHS Model is a space-efficient probabilistic data structure which utilized hashing function to create hash value for clustered data. Therefore, proposed MDC-BHS Technique significantly reduces the dimensionality of larger dataset. The experimental evaluation of MDC-BHS technique is carried out on weather data with factors such as clustering time and clustering accuracy and space complexity with respect to number of data. The experimental results demonstrate that MDC-BHS Technique is able to improve the clustering accuracy and also minimizes the space complexity when compared to state-of-the-art works

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Section: A Performance Results Of Clustering Accuracymentioning

confidence: 99%

“…LHNFCSF was not solved the time complexity of dimensionality reduction. A high-order CFS algorithm was intended in [9] with aiming at grouping heterogeneous data with higher accuracy. The true positive rate of clustering using high-order CFS algorithm was poor.…”

Section: Related Workmentioning

confidence: 99%

Moore Data Clustering Based Bloom Hash Storage for Dimensionality Reduction of Big Data Analytics

K*¹,

Maheswari.²

2019

IJRTE

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“…Many of these papers are about applications to a variety of scientific fields, another group seeks to improve its efficiency, and the final group widens the field of problems it can solve by hybridization with other procedures or by generalization. Examples of this third group are , which address detection of outliers by introducing the cutoff distance-based local density of each data point into the support-vector data-description (SVDD) training model and Bu et al (2016), which combine the DPC and the dropout deep-learning model to clustering heterogeneous data. apply the DPC to discovering social circles with overlap via user profile and topological structure-based features, and Liu et al (2017) and Du et al (2017) apply DPC to mixed data.…”

Section: Related Workmentioning

confidence: 99%

A methodology for automatic parameter-tuning and center selection in density-peak clustering methods

García-García

García-Ródenas

2020

Soft Comput

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The density-peak clustering algorithm, which we refer to as DPC, is a novel and efficient density-based clustering approach. The method has the advantage of allowing non-convex clusters, and clusters of variable size and density, to be grouped together, but it also has some limitations, such as the visual location of centers and the parameter tuning. This paper describes an optimization-based methodology for automatic parameter/center selection applicable both to the DPC and to other algorithms derived from it. The objective function is an internal/external cluster validity index, and the decisions are the parameterization of the algorithm and the choice of centers. The internal validation measures lead to an automatic parameter-tuning process, and the external validation measures lead to the so-called optimal rules, which are a tool to bound the performance of a given algorithm from above on the set of parameterizations. A numerical experiment with real data was performed for the DPC and for the fuzzy weighted k-nearest neighbor (FKNN-DPC) which validates the automatic parameter-tuning methodology and demonstrates its efficiency compared to the state of the art.

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RETRACTED ARTICLE: An efficient privacy preserving on high-order heterogeneous data using fuzzy K-prototype clustering

Dilip

2020

J Ambient Intell Human Comput

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A High-Order CFS Algorithm for Clustering Big Data

Cited by 6 publications

References 8 publications

Moore Data Clustering Based Bloom Hash Storage for Dimensionality Reduction of Big Data Analytics

Moore Data Clustering Based Bloom Hash Storage for Dimensionality Reduction of Big Data Analytics

A methodology for automatic parameter-tuning and center selection in density-peak clustering methods

RETRACTED ARTICLE: An efficient privacy preserving on high-order heterogeneous data using fuzzy K-prototype clustering

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