An Improved Version of K-medoid Algorithm using CRO

The emergence of geospatial big data has opened up new avenues for identifying urban environments. Although both geographic information systems (GIS) and expert systems (ES) have been useful in resolving geographical decision issues, they are not without their own shortcomings. The combination of GIS and ES has gained popularity due to the necessity of boosting the effectiveness of these tools in resolving very difficult spatial decision-making problems. The clustering method generates the functional effects necessary to apply spatial analysis techniques. In a symmetric clustering system, two or more nodes run applications and monitor each other simultaneously. This system is more efficient than an asymmetric system since it utilizes all available hardware and does not maintain a node in a hot standby state. However, it is still a major issue to figure out how to expand and speed up clustering algorithms without sacrificing efficiency. The work presented in this paper introduces an optimized hierarchical distributed k-medoid symmetric clustering algorithm for big data spatial query processing. To increase the k-medoid method’s efficiency and create more precise clusters, a hybrid approach combining the k-medoid and Chemical Reaction Optimization (CRO) techniques is presented. CRO is used in this approach to broaden the scope of the optimal medoid and improve clustering by obtaining more accurate data. The suggested paradigm solves the current technique’s issue of predicting the accurate clusters’ number. The suggested approach includes two phases: in the first phase, the local clusters are built using Apache Spark’s parallelism paradigm based on their portion of the whole dataset. In the second phase, the local clusters are merged to create condensed and reliable final clusters. The suggested approach condenses the data provided during aggregation and creates the ideal clusters’ number automatically based on the dataset’s structures. The suggested approach is robust and delivers high-quality results for spatial query analysis, as shown by experimental results. The proposed model reduces average query latency by 23%.

show abstract

“…The end goal is to transform it into a stable compound with the lowest feasible potential energy. For additional details, see [40].…”

Section: Proposed Methodsmentioning

confidence: 99%

Big Data Clustering Using Chemical Reaction Optimization Technique: A Computational Symmetry Paradigm for Location-Aware Decision Support in Geospatial Query Processing

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The main goal of the K‐means clustering is to form the K‐clusters with the minimized SSE value [35]. The steps for the K‐means clustering algorithm [36, 37] are shown in Figure 2. The K‐means algorithm is validated by measuring the silhouette value (SV).…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Recycled integrated circuit detection using reliability analysis and machine learning algorithms

Krishnan

Palanisamy

2020

IET Computers & Digital Tech

View full text Add to dashboard Cite

The use of counterfeit integrated circuits (ICs) in electronic products decreases its quality and lifetime. Recycled ICs can be detected by the method of aging analysis. Aging is carried out through reliability analysis with the effect of hot carrier injection and bias temperature instability (BTI). In this work, three machine learning methods, namely Kmeans clustering, back propagation neural network (BPNN) and support vector machines (SVMs), are used to detect the recycled IC aged for a shorter period (1 day) with minimum data size. This work also distinguishes the effects of degradation due to process variations and reliability effects. The reliability and Monte Carlo simulation are performed on benchmark circuits such as c17, s27, b02 and fully differential folded-cascode amplifier using the Cadence Virtuoso tool, and the parameters such as minimum voltage, delay value, supply current, gain, phase margin and bandwidth are measured. Machine learning methods are developed using MATLAB to train and classify the parameters. From the results obtained, it is observed that the classification rate for the benchmark circuits is 100%, and using BPNN, K-means clustering and SVM and the proposed method, recycled IC or used IC is detected even if it was used for 1 day.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

“…In another correlated study [41] proposed a variant of the genetic algorithm presented in 2004 that solves the k-medoids problem without considering a fixed k value, using for such a combination of a crossover operator with the Davies-Bouldin index. In [18] a hybrid algorithm is proposed that uses the CRO (Chemical Reaction Optimization) algorithm, applied to expand the search for the optimal medoid.…”

Section: Related Workmentioning

confidence: 99%

The effective BRKGA algorithm for the k-medoids clustering problem

Brito¹,

Semaan

Fadel

2022

RAIRO-Oper. Res.

View full text Add to dashboard Cite

This paper presents a biased random-key genetic algorithm for k-medoids clustering problem. A novel heuristic operator was implemented and combined with a parallelized local search procedure. Experiments were carried out with fifty literature data sets with small, medium, and large sizes, considering several numbers of clusters, showed that the proposed algorithm outperformed eight other algorithms, for example, the classics PAM and CLARA algorithms. Furthermore, with the results of a linear integer programming formulation, we found that our algorithm obtained the global optimal solutions for most cases and, despite its stochastic nature, presented stability in terms of quality of the solutions obtained and the number of generations required to produce such solutions. In addition, considering the solutions (clusterings) produced by the algorithms, a relative validation index (average silhouette) was applied, where, again, was observed that our method performed well, producing cluster with a good structure.

show abstract

An Improved Version of K-medoid Algorithm using CRO

Cited by 6 publications

References 17 publications

Big Data Clustering Using Chemical Reaction Optimization Technique: A Computational Symmetry Paradigm for Location-Aware Decision Support in Geospatial Query Processing

Big Data Clustering Using Chemical Reaction Optimization Technique: A Computational Symmetry Paradigm for Location-Aware Decision Support in Geospatial Query Processing

Recycled integrated circuit detection using reliability analysis and machine learning algorithms

The effective BRKGA algorithm for the k-medoids clustering problem

Contact Info

Product

Resources

About