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What is K-Means Clustering

Handbook of Research on Systems Biology Applications in Medicine
is an algorithm to group (cluster) objects based on certain attributes into a pre-determined number (K) of groups or clusters. The grouping is done by minimizing the sum of squares of distances between individual data and the corresponding cluster centre which is calculated by averaging all the data within the cluster. It is an iterative procedure that refines the groupings in multiple steps each improving the cluster quality.
Published in Chapter:
Data Integration for Regulatory Gene Module Discovery
Alok Mishra (Imperial College London, UK)
Copyright: © 2009 |Pages: 14
DOI: 10.4018/978-1-60566-076-9.ch030
Abstract
This chapter introduces the techniques that have been used to identify the genetic regulatory modules by integrating data from various sources. Data relating to the functioning of individual genes can be drawn from many different and diverse experimental techniques. Each piece of data provides information on a specific aspect of the cell regulation process. The chapter argues that integration of these diverse types of data is essential in order to identify biologically relevant regulatory modules. A concise review of the different integration techniques is presented, together with a critical discussion of their pros and cons. A very large number of research papers have been published on this topic, and the authors hope that this chapter will present the reader with a high-level view of the area, elucidating the research issues and underlining the importance of data integration in modern bioinformatics.
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C-MICRA: A Tool for Clustering Microarray Data
Objects are grouped into a fixed number (k) of partitions so that the partitions are dissimilar to each other.
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Cluster Analysis Using Rough Clustering and K-Means Clustering
A cluster analysis technique in which clusters are formed by randomly selecting k data points as initial seeds or centroids, and the remaining data points are assigned to the closest cluster on the basis of the distance between the data point and the cluster centroid.
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Cluster Analysis Using Rough Clustering and k-Means Clustering
A cluster analysis technique in which clusters are formed by randomly selecting k data points as initial seeds or centroids, and the remaining data points are assigned to the closest cluster on the basis of the distance between the data point and the cluster centroid.
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Smart IDS and IPS for Cyber-Physical Systems
Is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean serving as a prototype of the cluster.
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Metaheuristics Approaches to Solve the Employee Bus Routing Problem With Clustering-Based Bus Stop Selection
One of the simplest and popular unsupervised machine learning algorithms, which group similar data points together.
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A New Framework for Industrial Benchmarking
An unsupervised machine learning/data mining method for clustering a set of entities into clusters of similar entities.
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Explainable Safety Risk Management in Construction With Unsupervised Learning
An unsupervised technique for classifying observations within a dataset into a smaller number of clusters.
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Cluster Analysis in R With Big Data Applications
A clustering technique that identifies a predetermined number of clusters of similar size and shape through an iterative search process utilizing total distance from data points to cluster centers across all K clusters.
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Online Educational Video Recommendation System Analysis
Used to partition n observations into k groups, where each observation belongs to cluster with nearest mean.
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Mining Mobility Data in Response to COVID-19
A clustering method that aims to separate observations into k clusters through minimizing squared Euclidean distances in each cluster.
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Airbnb (Air Bed and Breakfast) Listing Analysis Through Machine Learning Techniques
A kind of algorithm that separates different data points to different clusters based on different values.
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Clustering Tourists Based on Reason for Destination Choice: Case of Izmir
Is an iterative divisive clustering technique that results in k specified clusters.
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Neural Networks for Automobile Insurance Pricing
An algorithm that performs disjoint cluster analysis on the basis of Euclidean distances computed from variables and randomly generated initial seeds.
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Cluster Analysis of Gene Expression Data
K-means clustering is the most well-known partition-based clustering algorithm. The algorithm starts by choosing k initial centroids, usually at random. Then the algorithm alternates between updating the cluster assignment of each data point by associating with the closest centroid and updating the centroids based on the new clusters until convergence.
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