DENCLUE-IM: A New Approach for Big Data Clustering

Rehioui, Hajar; Idrissi, Abdellah; Abourezq, Manar; Zegrari, Faouzia

doi:10.1016/j.procs.2016.04.265

Cited by 70 publications

(29 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…When implemented with a simulated data of a pliable peptide, it shows better efficacy than DBSCAN which is another type of density-based clustering algorithm. 17 Denclue follows the pseudocode and algorithm below as suggested by Kumar and Batra. 11 Problem: To determine density attractors and associated data objects using hill climbing, and merging the initial clusters if possible.…”

Section: Purpose Of the Studymentioning

confidence: 99%

“…The multiple Fuzzy-C means have been applied to health data set for medical diagnoses of headache, 28 BIRCH has been applied to cluster data sets of different time points, 24 and Denclue algorithm (Denclue-IM) has been used in spam base data set to classify e-mail as spam or nonspam. 17 Clustering analysis is limited in that there is no one clustering algorithm that works best for all solution. Also the use of traditional clustering algorithm with multilayer omics data which collect various types of omics information on the same subjects is challenging because while some clustering algorithms are good with text data, others are better with other types of data.…”

Section: Summary and Recommendationsmentioning

confidence: 99%

See 1 more Smart Citation

Gene-Based Clustering Algorithms: Comparison Between Denclue, Fuzzy-C, and BIRCH

Nwadiugwu

2020

Bioinform Biol Insights

View full text Add to dashboard Cite

The current study seeks to compare 3 clustering algorithms that can be used in gene-based bioinformatics research to understand disease networks, protein-protein interaction networks, and gene expression data. Denclue, Fuzzy-C, and Balanced Iterative and Clustering using Hierarchies (BIRCH) were the 3 gene-based clustering algorithms selected. These algorithms were explored in relation to the subfield of bioinformatics that analyzes omics data, which include but are not limited to genomics, proteomics, metagenomics, transcriptomics, and metabolomics data. The objective was to compare the efficacy of the 3 algorithms and determine their strength and drawbacks. Result of the review showed that unlike Denclue and Fuzzy-C which are more efficient in handling noisy data, BIRCH can handle data set with outliers and have a better time complexity.

show abstract

Section: Purpose Of the Studymentioning

confidence: 99%

Section: Summary and Recommendationsmentioning

confidence: 99%

Gene-Based Clustering Algorithms: Comparison Between Denclue, Fuzzy-C, and BIRCH

Nwadiugwu

2020

Bioinform Biol Insights

View full text Add to dashboard Cite

show abstract

“…These features of the Big memory open the way for powerful and memory-efficient parallel analyses and data mining of massive data sets [15]. Steps: choose k data items from D as initial centroids randomly [1] Repeat until no observation change [2] Assign each item d i to the cluster which has the closest centroid [3] Calculate the new mean for each cluster [4] Until convergence criteria are met [5] [3]…”

Section: Big K-means Clustering Algorithm With Shared Memorymentioning

confidence: 99%

“…The system design that deals with the massive volume of the data will also result in a system that can process a specific size of data set faster [2,3]. Recently, Rehioui H, et al [4] the authors propose The informational revolution has generated more terabytes of heterogeneous data every day. According to an investigation made by the institute IDC1, in the digital world 1.8 zettabyte data was created in 2011, 2.8 zettabytes in 2012 and it will increase up to 40 zettabytes in 2020 and more.…”

Section: Introductionmentioning

confidence: 99%

On the Solutions of the Big Data Timeliness Problem

Ebeid

Salem

Senousy

2019

AJRCoS

View full text Add to dashboard Cite

Big Data is increasingly used on almost the entire planet, both online and offline. It is not related only to computers. It makes a new trend in the decision-making process and the analysis of this data will predict the results based on the explored knowledge of big data using Clustering algorithms. The response time of performance and speed presents an important challenge to classify this monstrous data. K-means and big k-mean algorithms solve this problem. In this paper, researcher find the best K value using the elbow method, then use two ways in the first sequential processing and the second is parallel processing, then apply the K-mean algorithm and the big K-mean on shared memory to make a comparative study find which one is the best in different data sizes. The analysis performed by R studio environment.

show abstract

“…In [12] a new clustering algorithm was proposed. It shows good results in accordance with the three metrics of clustering (volume, variety, and veracity).…”

Section: Related Workmentioning

confidence: 99%

Weighted Clustering for Anomaly Detection in Big Data

Alguliyev

Alıguliyev

İmamverdiyev

et al. 2018

Stat., optim. inf. comput.

View full text Add to dashboard Cite

In this paper, a new method for anomaly detection based on weighted clustering is proposed. The weights that were obtained by summing the weights of each point from the data set are assigned to clusters. The comparison is made using seven datasets (of large dimensions) with the k-means algorithm. The proposed approach increases the reliability of data partitioning into groups. Experimental results show that the proposed approach becomes more efficient with increasing size of the analysed dataset.

show abstract

DENCLUE-IM: A New Approach for Big Data Clustering

Cited by 70 publications

References 18 publications

Gene-Based Clustering Algorithms: Comparison Between Denclue, Fuzzy-C, and BIRCH

Gene-Based Clustering Algorithms: Comparison Between Denclue, Fuzzy-C, and BIRCH

On the Solutions of the Big Data Timeliness Problem

Weighted Clustering for Anomaly Detection in Big Data

Contact Info

Product

Resources

About