An improvement of DENCLUE algorithm for the data clustering

Idrissi, Abdellah; Rehioui, Hajar; Laghrissi, Abdelquoddouss; Retal, Sara

doi:10.1109/icta.2015.7426936

Cited by 27 publications

(6 citation statements)

References 27 publications

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“…is approach, in contrast to partitioning and hierarchical clustering methods, defines clusters as a collection of points that are closely linked. Collection: the DBSCAN method, the DENCLUE algorithm, and other density clustering techniques are among the most widely used [17][18][19][20]. e DBSCAN method, for example, finds clusters by linking high-density neighborhood points in a given area.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Customer Demand for Hands-On Inquiry-Based Learning (HIBL) Product Based on Big Data Clustering Algorithm

Bai

2022

Security and Communication Networks

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The objective was to identify previously unknown groups in a dataset using various techniques. Significant progress has been made in this field in recent years, resulting in the development of novel and promising clustering algorithms. With the constant advancement of big data technology, research on study tours has also become crucial. Clustering can unearth the potential hidden information in large datasets, thereby facilitating more efficient work. Diverse measures have been proposed to quantify similarity, including the Euclidean distance and data space density. As a result, clustering becomes a multi-objective optimization problem. Clustering algorithms are extensively utilized in data preprocessing, data classification, and big data prediction. In this study, we examine clustering methods for big data from a theoretical perspective to comprehend their correlations across a large number of datasets. In addition, we predicted customer demand for research products using fabricated metrics.

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Section: Related Workmentioning

confidence: 99%

Prediction of Customer Demand for Hands-On Inquiry-Based Learning (HIBL) Product Based on Big Data Clustering Algorithm

Bai

2022

Security and Communication Networks

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“…Data points are allocated to clusters on the basis of their closeness and density interaction with the attractors as they are identified. The clustering procedure is repeated until no further attractors and clusters have been found [53].…”

Section: Density-based Clusteringmentioning

confidence: 99%

A Systematic Literature Review on Identifying Patterns Using Unsupervised Clustering Algorithms: A Data Mining Perspective

Chaudhry,

Shafi,

Mahnoor

et al. 2023

Symmetry

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Data mining is an analytical approach that contributes to achieving a solution to many problems by extracting previously unknown, fascinating, nontrivial, and potentially valuable information from massive datasets. Clustering in data mining is used for splitting or segmenting data items/points into meaningful groups and clusters by grouping the items that are near to each other based on certain statistics. This paper covers various elements of clustering, such as algorithmic methodologies, applications, clustering assessment measurement, and researcher-proposed enhancements with their impact on data mining thorough grasp of clustering algorithms, its applications, and the advances achieved in the existing literature. This study includes a literature search for papers published between 1995 and 2023, including conference and journal publications. The study begins by outlining fundamental clustering techniques along with algorithm improvements and emphasizing their advantages and limitations in comparison to other clustering algorithms. It investigates the evolution measures for clustering algorithms with an emphasis on metrics used to gauge clustering quality, such as the F-measure and the Rand Index. This study includes a variety of clustering-related topics, such as algorithmic approaches, practical applications, metrics for clustering evaluation, and researcher-proposed improvements. It addresses numerous methodologies offered to increase the convergence speed, resilience, and accuracy of clustering, such as initialization procedures, distance measures, and optimization strategies. The work concludes by emphasizing clustering as an active research area driven by the need to identify significant patterns and structures in data, enhance knowledge acquisition, and improve decision making across different domains. This study aims to contribute to the broader knowledge base of data mining practitioners and researchers, facilitating informed decision making and fostering advancements in the field through a thorough analysis of algorithmic enhancements, clustering assessment metrics, and optimization strategies.

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“…This method can detect clusters in any shape and is more resistant to noise and outliers since it is singlescanned. The typical example of this approach are: DBSCAN [45], DENCLUE [34], OPTICS [22] and DBCLASD [17].…”

Section: Density-based Clusteringmentioning

confidence: 99%

Big Data Clustering Techniques Challenged and Perspectives: Review

Awad¹,

Hamad²

2023

IJCAI

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Clustering in big data is considered a critical data mining and analysis technique. There are issues with adapting clustering algorithms to large amounts of data and new challenges brought by big data. As the size of big data is up to petabytes of data, and clustering methods have high processing costs, the challenge is how to handle this issue and utilize clustering techniques for big data efficiently. This study aims to investigate the recent advancement of clustering platforms and techniques to handle big data issues, from the early suggested techniques to today's novel solutions. The methodology and specific issues for building an effective clustering mechanism are presented and evaluated, followed by a discussion of the choices for enhancing clustering algorithms. A brief literature review of the recent advancement in clustering techniques has been presented to address each solution's main characteristics and drawbacks.Povzetek: Članek predstavlja pregled tehnik gručenja za velike podatke.

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An improvement of DENCLUE algorithm for the data clustering

Cited by 27 publications

References 27 publications

Prediction of Customer Demand for Hands-On Inquiry-Based Learning (HIBL) Product Based on Big Data Clustering Algorithm

Prediction of Customer Demand for Hands-On Inquiry-Based Learning (HIBL) Product Based on Big Data Clustering Algorithm

A Systematic Literature Review on Identifying Patterns Using Unsupervised Clustering Algorithms: A Data Mining Perspective

Big Data Clustering Techniques Challenged and Perspectives: Review

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