Data clustering using K-Means based on Crow Search Algorithm

Lakshmi, K.; Visalakshi, N. Karthikeyani; Shanthi, S. Anita

doi:10.1007/s12046-018-0962-3

Cited by 33 publications

(23 citation statements)

References 33 publications

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“…Definitely, clustering is a crucial independent arrangement procedure which is depicted by the allocating of a lot of models or vectors into the multi-dimensional space in bunches or assortment. Diverse closeness metrics between data objects are utilized to accomplish clustering; such data comparability/divergence in the database is viewed as utilizing distance estimation [40]. The activity is fueled by the possibility of data grouping using a specific amount of clusters by methods for distance belittlement among objects of each cluster itself.…”

Section: Overview Of Data Clustering and Bh Algorithm 21 The Problem Of Data Clusteringmentioning

confidence: 99%

A Hybrid Black Hole Algorithm with Genetic Algorithm for Solving Data Clustering Problems

Taloba¹

2021

TURCOMAT

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Clustering is a process of randomly selecting k-cluster centers also grouping the data around those centers. Issues of data clustering have recently received research attention and as such, a nature-based optimization algorithm called Black Hole (BH) has said to be suggested as an arrangement to data clustering issues. The BH as a metaheuristic which is elicited from public duplicates the black hole event in the universe, whereas circling arrangement in the hunt space addresses a solo star. Even though primordial BH has shown enhanced execution taking place standard datasets, it doesn't have investigation capacities yet plays out a fine local search. In this paper, another crossover metaheuristic reliant on the mix of BH algorithm as well as genetic algorithm suggested. Genetic algorithm represents its first part of the algorithm which prospects the search space and provides the initial positions for the stars. Then, the BH algorithm utilizes the search space and finds the best solution until the termination condition is reached. The proposed hybrid approach was estimated on synchronized nine popular standard functions where the outcomes indicated that the process generated enhanced outcome with regard to robustness compared to BH and the benchmarking algorithms in the study. Furthermore, it also revealed a high convergence rate which used six real datasets sourced of the UCI machine learning laboratory, indicating fine conduct of the hybrid algorithm on data clustering problems. Conclusively, the investigation showed the suitability of the suggested hybrid algorithm designed for resolving data clustering issues.

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Section: Overview Of Data Clustering and Bh Algorithm 21 The Problem Of Data Clusteringmentioning

confidence: 99%

A Hybrid Black Hole Algorithm with Genetic Algorithm for Solving Data Clustering Problems

Taloba¹

2021

TURCOMAT

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“…datasets, the engines are operating at different operational regimes. K-Means clustering algorithm will be used to cluster the data as it is the most widely used and easy to implement clustering algorithms [90]. Its advantages and constraints can be summarized as follows:…”

Section: Data Clusteringmentioning

confidence: 99%

“…Based on the features of this algorithm and the nature of the data dealt with in this chapter, the K-Means algorithm will be ideal for clustering. This algorithm partitions the data features into a predefined number of clusters based on their distance from the centroid [90]. It does that by trying to minimize the total intra-cluster distance or the squared error function which can be calculated using equation 3.1.…”

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confidence: 99%

Fault Detection, Isolation and Prognosis of Aerospace Systems Using Adaptive Growing Recurrent Neural Networks

ElDali¹

2021

Preprint

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Due to the increase in complexity in aerospace systems, developing a diagnosis, prognosis and health monitoring (DPHM) framework is a challenge that must be considered to assure the safety of such systems. This thesis discusses this problem by proposing a novel growing neural network model to automate the process of DPHM for aerospace systems. The model optimizes the architecture of a recurrent neural network and was used to make Remaining Useful Lifetime (RUL) predictions for aircraft engines and detect failure for satellite attitude actuators (Reaction Wheels). It was tested on the CMAPSS and PHM08 aircraft engine datasets simulated by NASA, and it was able to make RUL predictions with root mean square errors as low as 14.31 engine cycles. Another application to test the proposed model was on the Kepler Spacecraft’s reaction wheels from which two have failed. The model detected the failure of the two failed reaction wheels by estimating a Health Index value which indicates the probability of failure of the reaction wheels using the residuals between the speed predictions made by the model and measured speed values. Failure was predicted using the model 105 days and 54 days before it occurred for reaction wheels two and four respectively. Prognostics were also applied on the Kepler Mission reaction wheels and RUL predictions were made with mean absolute errors ranging between 2-13 days depending on how close the reaction wheel is to failure. The proposed algorithm showed results in both applications that could regard it as a promising approach for DPHM models.

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“…Suggestion frameworks have demonstrated to be valuable methods to proof this problem and support customers to find what they want in a sensible time. Recommender software filters data using various calculations and prescribes the most applicable things to clients [6]. The primary thought behind the recommender framework is to utilize clients' past inclination to foresee future interests of clients.…”

Section: Introductionmentioning

confidence: 99%

“…Understanding the online user's needs and desires is viewed as a significant for the present customer situated electronic business showcase. So, to overcome the problems facing in traditional recommender systems, a lot of research papers [ 6,8,11,12,13,14,18,19,20], have been done in CF by combining traditional recommendation approaches with that of modern recommendation approaches such as semantic based approaches and cross domain based approaches. The rest of this paper work is arranged as pursues: Section 2 describes related work.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Collaborative Recommender System by Crow Search Algorithm and K-Means algorithm

Tesfaye¹,

Pooja²,

Astya³

2019

IJRTE

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A recommender framework is a data refining engines that seeks to foresee the rating for customers and things from enormous information to suggest their preferences. Movie suggestion frameworks give a system to help customers in arranging customers with practically identical interests. This causes a recommender framework basically a focal piece of sites and internet business application. In this study, we have developed a collaborative movie recommender system using crow search and K-means algorithm. This article centers on the movie suggestion proposal frameworks whose essential goal is to recommend a recommender framework through information bunching and computational insight. We have used Elbow method and Silhouette score to select right k number of clusters and calculate errors in each cluster respectively. We have used evaluation metrics standard deviation, mean absolute error, and root mean absolute error to evaluate the performance of the proposed system. The experiment result shows 0.635 MAE and 0.758 RMSE which indicates that our framework accomplished better execution contrast with other existing approaches.

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Data clustering using K-Means based on Crow Search Algorithm

Cited by 33 publications

References 33 publications

A Hybrid Black Hole Algorithm with Genetic Algorithm for Solving Data Clustering Problems

A Hybrid Black Hole Algorithm with Genetic Algorithm for Solving Data Clustering Problems

Fault Detection, Isolation and Prognosis of Aerospace Systems Using Adaptive Growing Recurrent Neural Networks

Intelligent Collaborative Recommender System by Crow Search Algorithm and K-Means algorithm

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