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What is K-Means

Chapter 13
Advancing SMEs Toward E-Commerce Policies for Sustainability
K-means clustering is a method of vector quantization, originally from signal processing, that seeks to partition n observations into k clusters in which each observation belongs to the cluster containing the nearest mean (cluster centers or cluster centroid), which serves as a prototype for the cluster.
Published in Chapter:
Customer Segmentation of Shopping Mall Users Using K-Means Clustering
Amit Kumar (Andhra University, India)
Copyright: © 2023 |Pages: 23
DOI: 10.4018/978-1-6684-5727-6.ch013
Abstract
The most successful companies are the one that know their customers very well and can anticipate their needs. Good customer profiles at the fingertips have businesses improve marketing campaigns, targeting feature launches and product roadmaps. In this study, exploratory data analysis was done on the shopping mall data, and customer segmentation was done using k-means clustering. Two different clusters were done based on age vs. spending score and annual income vs. spending score. Four optimum clusters were obtained for age and spending scores using the elbow graph method, and five optimum clusters were obtained for annual income and spending scores. Firstly, for clusters based on age and spending score, people with higher age groups have less spending scores. Secondly, clusters based on annual income and spending scores had high annual income and very low spending scores. Thus, the mall can offer these cluster customers to attract them, thereby increasing its profits.
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Chapter 393
Machine Learning Through Data Mining
Iterative clustering technique subdividing the data in such a way to maximize the distance among centroids of different clusters, while minimizing the distance among data within each cluster. It is sensitive to initialization.
Published in Chapter: Machine Learning Through Data Mining; From: Encyclopedia of Information Science and Technology, Second Edition
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Chapter 3
Mapping the Collaborative Platform Economy Business Practice: A Typological Study
A unsupervised machine learning algorithm for classification. It identifies the k number of centroids and then allocates every sample data point to the nearest cluster.
Published in Chapter: Mapping the Collaborative Platform Economy Business Practice: A Typological Study; From: Handbook of Research on the Platform Economy and the Evolution of E-Commerce
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Chapter 33
Applications for Data Mining Techniques in Customer Relationship Management
A nonhierarchical clustering technique which splits data sets into K (a given number) subsets in a way that each subset is maximally compact.
Published in Chapter: Applications for Data Mining Techniques in Customer Relationship Management; From: Encyclopedia of Information Science and Technology, Second Edition
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Chapter 344
Clustering Methods for Detecting Communities in Networks
A clustering algorithm to partition n observations into K clusters, which each K cluster has a centroid and each observation is assigned to a cluster.
Published in Chapter: Clustering Methods for Detecting Communities in Networks; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 130
System Theory: From Classical State Space to Variable Selection and Model Identification
Iterative clustering technique subdividing the data in such a way to maximize the distance among centroids of different clusters, while minimizing the distance among data within each cluster. It is sensitive to initialization.
Published in Chapter: System Theory: From Classical State Space to Variable Selection and Model Identification; From: Encyclopedia of Information Technology Curriculum Integration
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Chapter 122
KD-Tree Based Clustering for Gene Expression Data
A well known partitional clustering algorithm which depends on the random initialization of the cluster centers.
Published in Chapter: KD-Tree Based Clustering for Gene Expression Data; From: Encyclopedia of Business Analytics and Optimization
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Chapter 7
Intelligent Log Analysis Using Machine and Deep Learning
Machine learning technique that identifies groups/clusters of data points that are similar to each other.
Published in Chapter: Intelligent Log Analysis Using Machine and Deep Learning; From: Machine Learning and Cognitive Science Applications in Cyber Security
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Chapter 6
Holistic View on Detecting DDoS Attacks Using Machine Learning
Unsupervised method used for classification and regression problems, that groups data by assigning all data points to the closest clusters, then determining the cluster means.
Published in Chapter: Holistic View on Detecting DDoS Attacks Using Machine Learning; From: Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity
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