tailieunhanh - Distance based k-means clustering algorithm for determining number of clusters for high dimensional data

To improve the clustering task on high dimensional data sets, the distance based k-means algorithm is proposed. The proposed algorithm is tested using eighteen sets of normal and non-normal multivariate simulation data under various combinations. | Nội dung Text Distance based k-means clustering algorithm for determining number of clusters for high dimensional data Decision Science Letters 9 2020 51 58 Contents lists available at GrowingScience Decision Science Letters homepage dsl Distance based k-means clustering algorithm for determining number of clusters for high dimensional data Mohamed Cassim Alibuhttoa and Nor Idayu Mahatb a Department of Mathematical Sciences Faculty of Applied Sciences South Eastern University of Sri Lanka Sri Lanka b Department of Mathematics and Statistics School of Quantitative Sciences Universiti Utara Malaysia Malaysia CHRONICLE ABSTRACT Article history Clustering is one of the most common unsupervised data mining classification techniques for Received March 23 2019 splitting objects into a set of meaningful groups. However the traditional k-means algorithm is Received in revised format not applicable to retrieve useful information clusters particularly when there is an August 12 2019 overwhelming growth of multidimensional data. Therefore it is necessary to introduce a new Accepted August 12 2019 Available online strategy to determine the optimal number of clusters. To improve the clustering task on high August 12 2019 dimensional data sets the distance based k-means algorithm is proposed. The proposed algorithm Keywords is tested using eighteen sets of normal and non-normal multivariate simulation data under various Clustering combinations. Evidence gathered from the simulation reveal that the proposed algorithm is High Dimensional Data capable of identifying the exact number of clusters. K-means algorithm Optimal Cluster Simulation 2020 by the authors licensee Growing Science Canada. 1. Introduction The amount of data collected daily is increasing but only part of the data that can be used to extract information which are valuable. This has led to data mining a process of extracting interesting and useful information in the form of relations and .

• Cơ khí - Chế tạo máy
• High Dimensional Data
• K means algorithm
• Distance based k means algorithm
• Normal and non normal multivariate simulation data
• Distance based k means clustering algorithm
• BMC Bioinformatics
• Personalized medicine
• Three dimensional space
• Interactive multidimensional scaling
• Radial visualization
• Quality visualization
• Supervised visualization
• New variant of radial visualization
• Multivariate analysis
• High dimensional omics data
• Partial least squares
• Biological pathways
• Dimension reduction
• Stability selection
• Principal covariates regression
• Missing value
• Truncated normal
• K nearest neighbors
• Model selection
• Model complexity
• Prediction error curves
• Random forest
• Time to event settings
• BMC Genomics
• Nonlinear correlation
• RV coefficient
• Detecting dependence
• Information and communication technology
• Feature clustering for pso based feature construction
• Particle swarm optimisation
• Feature construction
• BMC Genetics
• Machine learning
• Recursive feature elimination
• Causal variables
• Lecture Data mining
• Bài giảng Khai thác dữ liệu
• Data mining
• Dimensionality reduction
• High dimensional datasets
• Multi dimensional scaling
• High dimensional
• Negative binomial
• Global test
• RNA Seq
• Error control
• Variable selection
• Modern biotechnologies
• High dimensional data sets
• Weighted regression
• Subgroup analysis
• Time to event endpoint
• Lecture Data mining and analysis
• Khai thác dữ liệu
• Data analysis foundations
• Frequent pattern mining
• High dimensional model representation
• Disease prediction
• Log likelihood ratio
• Expression analysis
• High dimension
• Predictive value
• Classification methods
• Clinical data
• Single Cell
• Genetic algorithms
• Feature selection
• Class prediction
• Plasma miRNAs
• Cluster analysis
• Clustering high dimensional data
• Frequent term based document clustering
• Automated synthesis
• Symbolic decision procedures
• High fidelity visualization
• Biomedical informatics
• Flow cytometry
• Compositional data
• High dimensionality
• Centered log ratio transformation
• Multisample test
• Distance correlation
• Time course data
• Longitudinal data
• Multiple solutions