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What is K-Means Algorithm

Chapter 24
Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics
It is an algorithm that is used for cluster analysis.
Published in Chapter:
Clustering by K-Means Method and K-Medoids Method: An Application With Statistical Regions of Turkey
Onur Önay (School of Business, Istanbul University, Turkey)
Copyright: © 2021 |Pages: 32
DOI: 10.4018/978-1-7998-3053-5.ch024
Abstract
Data science and data analytics are becoming increasingly important. It is widely used in scientific and real-life applications. These methods enable us to analyze, understand, and interpret the data in every field. In this study, k-means and k-medoids clustering methods are applied to cluster the Statistical Regions of Turkey in Level 2. Clustering analyses are done for 2017 and 2018 years. The datasets consist of “Distribution of expenditure groups according to Household Budget Survey” 2017 and 2018 values, “Gini coefficient by equivalised household disposable income” 2017 and 2018 values, and some features of “Regional Purchasing Power Parities for the main groups of consumption expenditures” 2017 values. Elbow method and average silhouette method are applied for the determining the number of the clusters at the beginning. Results are given and interpreted at the conclusion.
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More Results
Chapter 18
Healthcare-Internet of Things and Its Components: Technologies, Benefits, Algorithms, Security, and Challenges
It is an iterative algorithm that partition the hole data set into K non overlaping subsets (Clusters). Each data point belongs to only one subset.
Published in Chapter: Healthcare-Internet of Things and Its Components: Technologies, Benefits, Algorithms, Security, and Challenges; From: Optimizing Health Monitoring Systems With Wireless Technology
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Chapter 14
Remote Sensing Image Classification Using Fuzzy-PSO Hybrid Approach
Clustering algorithm to classify n elements in k clusters, which iteratively computes the cluster centroids as the means of all elements in one cluster.
Published in Chapter: Remote Sensing Image Classification Using Fuzzy-PSO Hybrid Approach; From: Handbook of Research on Swarm Intelligence in Engineering
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Chapter 27
Cancer Gene Expression Data Analysis Using Rough Based Symmetrical Clustering
Clustering algorithm to classify n elements in k clusters, which iteratively computes the cluster centroids as the means of all elements in one cluster.
Published in Chapter: Cancer Gene Expression Data Analysis Using Rough Based Symmetrical Clustering; From: Handbook of Research on Computational Intelligence for Engineering, Science, and Business
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Chapter 11
Voronoi-Based kNN Queries Using K-Means Clustering in MapReduce
k -means algorithm is the well-known and commonly used clustering method. The algorithm takes the input parameter k and partitions a set X of n data points D in R d into k clusters.
Published in Chapter: Voronoi-Based kNN Queries Using K-Means Clustering in MapReduce; From: Emerging Technologies and Applications in Data Processing and Management
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Chapter 8
Segmentation of Cardiac Magnetic Resonance Images
The k-means algorithm is an algorithm to cluster n objects based on attributes into k partitions, k = n. The algorithm minimizes the total intra-cluster variance or the squared error function.
Published in Chapter: Segmentation of Cardiac Magnetic Resonance Images; From: Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications
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