Research ArticleOpen accessPhrase in the titleData Clustering using Two-Stage Eagle Strategy Based on Slime Mould AlgorithmRachid Oucheikh, Achraf Touil, Mouhsene FriDataclustering is considered an important component of data mining which aims tosplit a given dataset into disjoint groups having the same similarities. Thedeveloped techniques for clustering have some challenges to cluster entities incomplex search space and most of them aim to maximize the sum of inter-clusterdistances and minimize the sum of intra-cluster distances. This objectivefunction is nonlinear and hard to … …Journal of Computer Science, Vol. 18, Issue 11 · 7 Nov 2022 · 5,791 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleData Clustering-based Metaheuristic for Physical Internet Supply Chain NetworkAbdelsamad Chouar, Samir Tetouani, Aziz Soulhi, Jamila ElalamiIn this study, a data clustering-driven technique is proposed for a Physical Internet Supply Chain Network (PI-SCN) to reduce data complexity, process time compression, and lankness of process optimization. Given a set of data points, a clustering algorithm aims to classify each data-points into a specific group. Each group should have similar properties and/or features, while data points in different … …Journal of Computer Science, Vol. 18, Issue 4 · 21 Apr 2022 · 6,472 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleModified ACS Centroid Memory for Data ClusteringAyad Mohammed Jabbar, Ku Ruhana Ku-Mahamud, Rafid SagbanAnt Colony Optimization (ACO) is a generic algorithm, which has been widely used in different application domains due to its simplicity and adaptiveness to different optimization problems. The key component that governs the search process in this algorithm is the management of its memory model. In contrast to other algorithms, ACO explicitly utilizes an adaptive memory, which is important to … …Journal of Computer Science, Vol. 15, Issue 10 · 11 Oct 2019 · 7,641 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleAn Elite Pool-Based Big Bang-Big Crunch Metaheuristic for Data ClusteringIbrahim Al-Marashdeh, Ghaith M. Jaradat, Masri Ayob et al.This paper delves into the capacity of enhanced Big Bang-Big Crunch (EBB-BC) metaheuristic to handle data clustering problems. BB-BC is a product of an evolution theory of the universe in physics and astronomy. Two main phases of BB-BC are big bang and big crunch. The big bang phase involves a creation of a population of random initial solutions, while in … …Journal of Computer Science, Vol. 14, Issue 12 · 19 Jun 2018 · 7,879 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleAn Integrated Framework for Mixed Data Clustering Using Self Organizing MapHari Prasad Devaraj, M. PunithavalliProblem statement: Clustering plays an important role in data mining of large data and helps in analysis. This develops a vast importance in research field for providing better clustering technique. There are several techniques exists for clustering the similar kind of data. But only very few techniques exists for clustering mixed data items. This leads to the requirement of better … …Journal of Computer Science, Vol. 7, Issue 11 · 18 Aug 2011 · 7,304 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleFractional Lion Algorithm-An Optimization Algorithm for Data ClusteringSatish Chander, P. Vijaya, Praveen DhyaniClustering divides the data available as bulk into meaningful, useful groups (Clusters) without any prior knowledge about the data. Cluster analysis provides an abstraction from individual data objects to the clusters in which those objects reside. It is a key technique in the data mining and has become an important issue in many fields. This paper presents a novel Fractional … …Journal of Computer Science, Vol. 12, Issue 7 · 12 Aug 2016 · 7,673 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleA Density Based Dynamic Data Clustering Algorithm based on Incremental DatasetS. Angel Latha Mary, K.R. Shankar KumarProblem statement: Clustering and visualizing high-dimensional dynamic data is a challenging problem. Most of the existing clustering algorithms are based on the static statistical relationship among data. Dynamic clustering is a mechanism to adopt and discover clusters in real time environments. There are many applications such as incremental data mining in data warehousing applications, sensor network, which relies on dynamic … …Journal of Computer Science, Vol. 8, Issue 5 · 20 Feb 2012 · 7,465 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleA New Cooperative Algorithm Based on PSO and K-Means for Data ClusteringMehdi Neshat, Shima Farshchian Yazdi, Daneyal Yazdani, Mehdi SargolzaeiProblem statement: Data clustering has been applied in multiple fields such as machine learning, data mining, wireless sensor networks and pattern recognition. One of the most famous clustering approaches is K-means which effectively has been used in many clustering problems, but this algorithm has some drawbacks such as local optimal convergence and sensitivity to initial points. Approach: Particle Swarm Optimization … …Journal of Computer Science, Vol. 8, Issue 2 · 19 Nov 2011 · 7,967 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleArtificial Bee Colony Algorithm Integrated with Fuzzy C-mean Operator for Data ClusteringM. Krishnamoorthi, A. M. NatarajanClustering task aims at the unsupervised classification of patterns in different groups. To enhance the quality of results, the emerging swarm-based algorithms now-a-days become an alternative to the conventional clustering methods. In this study, an optimization method based on the swarm intelligence algorithm is proposed for the purpose of clustering. The significance of the proposed algorithm is that it uses … …Journal of Computer Science, Vol. 9, Issue 4 · 6 May 2013 · 6,299 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleAdaptive Data Transformation for Enhanced Clustering Performance in Diagnostic SystemsMohammed Subhi Al-BatahThis paper presents an enhanced approach to the Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm, aimed at improving clustering accuracy in medical data, specifically for breast cancer diagnosis. The proposed method introduces a modified data transformation technique to optimize the original BIRCH algorithm. This transformation refines the clustering process, resulting in significant improvements in diagnostic accuracy. The modified … …Journal of Computer Science, Vol. 21, Issue 9 · 17 Oct 2025 · 2,287 accessesView PDFCite