An efficient k-means clustering filtering algorithm using density based initial cluster centers

Kumar, Kanike Vinod; Reddy, A. Rama Mohan

doi:10.1016/j.ins.2017.07.036

Cited by 83 publications

(41 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…[26]. The initial clustering center of K-means clustering algorithm chooses the point under the high density as the initial clustering center to achieve better clustering effect [27] . When the number of tasks is far greater than the number of virtual machines, the polling mechanism is used to establish the equal subgraph of task and virtual machine.…”

Section: Related Workmentioning

confidence: 99%

Resource Constrained Profit Optimization Method for Task Scheduling in Edge Cloud

et al. 2020

View full text Add to dashboard Cite

Edge cloud is a cloud computing system built on edge infrastructure. Task scheduling optimization is the key technology to ensure the quality of service in edge cloud. However, the openness of the edge cloud environment challenges the load balancing and profit optimization of task scheduling. In this paper, we analyze the business process and optimization factors of task scheduling in edge cloud. First, we propose a resource constrained task scheduling profit optimization algorithm (RCTSPO), which consists of clustering preprocessing, classification, profit matrix construction and optimal scheduling strategy calculation. Clustering preprocessing gathers similar tasks into one class and perform a classification on the clustered tasks. Then construct the profit matrix for resource constrained task scheduling, and the optimal task scheduling strategy is obtained based on the constructed profit matrix. Second, Petri nets are used to construct the different components of edge cloud, such as resource, task, user request and virtual machine, thus forming the task scheduling model of edge cloud. Third, the properties of task scheduling model are verified by using the related theory and tools of Petri nets. Finally, several experiments are done to evaluate the proposed method, the simulation results show that the algorithm not only achieves the maximum profit, but also performs well in terms of time, reliability and load balancing of task scheduling.

show abstract

Section: Related Workmentioning

confidence: 99%

Resource Constrained Profit Optimization Method for Task Scheduling in Edge Cloud

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Golasowski et al [34] Cluster initialization method based on Brute-force approach using heuristics Kumar and Reddy [35] Density based initialization method which is also scalable to large datasets…”

Section: Goyal Andmentioning

confidence: 99%

MP-K-Means: Modified Partition Based Cluster Initialization Method for K-Means Algorithm

Gupta*,

Chandra

2019

IJRTE

View full text Add to dashboard Cite

In k-means algorithm, initial cluster centroids are selected arbitrarily which leads to diverse formation of clusters in each run. Consequently, accuracy and performance of k-means is majorly depends on the selection of initial centroids. Thus, the initial cluster centroids shall be chosen carefully to obtain better accuracy and performance of k-means algorithm. In view of this, a new Modified Partition based Cluster Initialization method for k-means called as MP-k-means is proposed in this paper. MP-k-means is an amended version of P-k-means [1] in which the range of values of each dimension is divided into ‘k’ equi-sized partition based on arithmetic average. This division of range into ‘k’ equi-sized partition is affected by outliers present in the data. In order to remove the effect of outliers in P-k-means, the partitioning of each dimension is made based on positional average instead of arithmetic average in MP-k-means. Six popular datasets are used for empirical evaluation of the algorithms. The empirical results are compared and validated based on various external and internal clustering validation measures. The comparative results show that MP-k-means is significantly superior to the basic k-means and P-k-means. The proposed method may also be applied to other clustering algorithms which are based on the concept of selection of initial cluster centroids.

show abstract

“…Disadvantage: The significant limitation of this technique is when the difference between the gene densities is maximum, then it's not able to cluster accurately [13]. • Graph based clustering: It's a kind of clustering technique and graph structure is formed by group of vertices and edges that are connected between the pair of vertices.…”

Section: • Density Based Techniquementioning

confidence: 99%

Review on Clustering Cancer Genes

Prabhuraj¹,

Shastry²,

Patil³

2019

IJCET

View full text Add to dashboard Cite

Present studies, development of genomic technologies are highly concentrated on galactic scale gene data. In Bioinformatics community, the sizable volume of gene data investigation and distinguishing the behavior of genes in antithetical conditions are the intriguing task. This cognitive factor can be deal by the clustering technique, its groups the similarity patterns at various features. Moreover, gene expression data indicates the contrastive levels of gene behaviors in various tissue cells and it does provide the feature information effectively. This gene clustering investigation is precise and accommodating in cancer uncovering because of its easiness to detect the cancerous and non-cancerous genes. The precautionary measures cancer diagnostic is precise crucial for cancer prevention and treatment. The existing cancer gene clustering techniques includes several limitations such as time complexities in training and testing samples, maximum redundant features and high dimensional data. These issues are severely influences the data clustering accuracy. This paper focuses on survey of various clustering techniques of cancer gene clustering with respect to cancer gene benchmark datasets. Furthermore, review of existing cancer gene clustering technique describes the advantages and limitations comprehensively.

show abstract

An efficient k-means clustering filtering algorithm using density based initial cluster centers

Cited by 83 publications

References 18 publications

Resource Constrained Profit Optimization Method for Task Scheduling in Edge Cloud

Resource Constrained Profit Optimization Method for Task Scheduling in Edge Cloud

MP-K-Means: Modified Partition Based Cluster Initialization Method for K-Means Algorithm

Review on Clustering Cancer Genes

Contact Info

Product

Resources

About