A review for the efficient clustering based on distance and the calculation of centroid

Kumari, Isha; Sharma, Vivek

doi:10.19101/ijatee.2020.762021

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…In the present scenario in every field there is huge amount of data gathered, processed and extracted for different purposes [1][2][3]. To acquire the data in the meaningful way is very important [4][5][6]. The main areas are health care, education, business enterprises and so on.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Class-Based Data Clustering Through K-Means And Knn Approach

Patidar¹

2021

TURCOMAT

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In this paper an efficient class-based data clustering through k-means and KNN approach were applied. It has been applied for proper data grouping and efficient classification. In this approach three dataset have been considered for the experimentation. Data preprocessing has been performed for the removal of unmatched or empty entry. Then weight assignment and normalization has been performed. K-means and k-nearest neighbor (KNN) have been applied on the dataset for the data grouping and classification purpose. Different splitting variations have been considered with higher variance. Data selection is completely random. The results obtained shows the strength of our approach though classification and class-based clustering.

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Section: Introductionmentioning

confidence: 99%

An Efficient Class-Based Data Clustering Through K-Means And Knn Approach

Patidar¹

2021

TURCOMAT

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“…A review and analysis the method is used to find the better clustering mechanism to get high efficiency. Different methods have been studied and discussed their advantages are highlighted to provide help the selection of and efficient clustering mechanism [18]. The possibilities of a Wireless Sensor Network support (WSN) for a Fog computing system in disposable intelligent wireless sensors is presented.…”

Section: Introductionmentioning

confidence: 99%

Wireless Sensor Networks Simulation Model to Compute Verification Time in Terms of Groups for Massive Crowd

Nawaz¹,

Al-Zghoul²,

Malik³

et al. 2021

Adv. sci. technol. eng. syst. j.

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Everyone needs fast response or output against its request or need. Therefore, technologies are used to make the processing fast and accurate according to our needs. But in some situations, still we need to do more. Especially, when we need to process a massive or huge crowd of people in limited time frame such as at airport, religious gatherings, at stations etc. As the verification time increases crowd also increases and causing problems such as missing of the next flight, causality in religious gathering. Also, percentage of usefulness of verification system decreases and vice versa. Currently, different approaches are being employed to reduce verification time such as decentralized, distributed, queuing etc. But each of the approach has its merits and demerits. In order to minimize this problem, one of the solutions is to perform group (cluster) based verification. The reason behind is that as religious or tourist visits are done in groups, therefore, we can easily perform the group verification to make the verification fast in context of time. Further, we can set the limit where we need the group verification and where we can go through the normal verification (one by one). In this paper, we presented a Wireless Sensor Network (WSN) simulation model to calculate verification time in the groups form for massive crowd. We discussed the different scenarios for a group such as all group (cluster) members (CMs) are same and within the range or out of the range of cluster head (CH), CMs and non-CMs are within the range or out of range of the CH. We considered the pilgrimage as use case to compare the verification time taken by existing system and proposed system in context of time. We also compared the verification time with respect to verified and unverified CMs (CMs) in a group verification model. By optimizing the number of CM members in a group will decrease the number of unverified CMs (the drop rate), hence the performance of the group verification will be increased by minimizing the group verification time.

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An efficient distance estimation and centroid selection based on k-means clustering for small and large dataset

Ladha¹,

Pippal²

2020

IJATEE

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In this paper an efficient distance estimation and centroid selection based on k-means clustering for small and large dataset. Data pre-processing was performed first on the dataset. For the complete study and analysis PIMA Indian diabetes dataset was considered. After pre-processing distance and centroid estimation was performed. It includes initial selection based on randomization and then centroids updations were performed till the iterations or epochs determined. Distance measures used here are Euclidean distance (Ed), Pearson Coefficient distance (PCd), Chebyshev distance (Csd) and Canberra distance (Cad). The results indicate that all the distance algorithms performed approximately well in case of clustering but in terms of time Cad outperforms in comparison to other algorithms.

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A review for the efficient clustering based on distance and the calculation of centroid

Cited by 4 publications

References 18 publications

An Efficient Class-Based Data Clustering Through K-Means And Knn Approach

An Efficient Class-Based Data Clustering Through K-Means And Knn Approach

Wireless Sensor Networks Simulation Model to Compute Verification Time in Terms of Groups for Massive Crowd

An efficient distance estimation and centroid selection based on k-means clustering for small and large dataset

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