Hyperspectral image analysis for subcutaneous veins localization
Ahmad Fadzil Mohamad Hani, Dileep Kumar in Optical Imaging for Biomedical and Clinical Applications, 2017
Clustering algorithms can be further classified into two types. One is hard clustering algorithms such as k-means and second is fuzzy (or soft) clustering algorithms such as fuzzy c-mean (FCM) algorithm. Both k-means and FCM are widely used algorithms for clustering of data into certain number of clusters. The difference lies in the probabilities of data belonging to clusters. Since k-means is a hard clustering algorithm, data can belong to a single cluster only by making the probability of one at its cluster and zero in any other cluster. In soft clustering, a membership degree is defined as probability of data belonging to any cluster. A membership function is used to determine the membership degrees. In this way, a data point can simultaneously belong to a number of clusters, but the parent cluster is chosen on the basis of highest membership degree [26]. Since soft clustering is more natural than hard clustering, it has been chosen for the classification of skin tone. In case of data points on cluster boundaries, the method allows data belonging to more than one cluster based on membership degree.
Basic Approaches of Artificial Intelligence and Machine Learning in Thermal Image Processing
U. Snekhalatha, K. Palani Thanaraj, Kurt Ammer in Artificial Intelligence-Based Infrared Thermal Image Processing and Its Applications, 2023
Hierarchical Clustering, also known as AGNES (agglomerative nesting), is a bottom-up clustering approach. The Data points present inside each cluster are as proximate to each other as possible, and data points from different clusters are as distant from each other. In hierarchical clustering, pairs of clusters are merged as they move up the hierarchy. The algorithm can be explained as follows:Each data point or pixel is made as a single point cluster. At this point, we have N number of clusters.Two data points closest to each other are taken and merged to form one cluster. Now, we have N − 1 clusters.Again, take any two close single point clusters and merge them to form a bigger cluster. This gives us N − 2 clusters.The above steps are repeated until there is only one big cluster.The hierarchical clustering algorithm is often represented in a graph that is known as a dendrogram, which can either be a column or a row graph. It represents the hierarchical relationships between different sets of data. By looking at a dendrogram, it is possible to understand how each cluster was formed.
Latent Class Analysis
Douglas D. Gunzler, Adam T. Perzynski, Adam C. Carle in Structural Equation Modeling for Health and Medicine, 2021
In traditional clustering algorithms such as standard k-means or nearest neighbor, clustering is typically based on some form of a distance metric (e.g. squared Euclidean distance). Since a mixture model is a statistical model, one can practically handle missing values and categorical variables in mixture modeling. Even when all items are continuous, unlike the k-means clustering approach, the items used in the analysis do not need to have the same scale or equal variances. In addition, mixture modeling allows one to quantify relative fit of a model and statistically test between solutions to help determine the number of subgroups for observed data [4]. The decision to rule in favor of a particular solution for a particular model is thus less subjective than in traditional clustering algorithms [4].
Trends in Healthcare Research on Visual Impairment and Blindness: Use of Bibliometrics and Hierarchical Cluster Analysis
Published in Ophthalmic Epidemiology, 2021
Hierarchical cluster analysis is a widely used clustering technique.14 Cluster analysis or clustering is an unsupervised machine learning technique that entails partitioning observations into similar groups or clusters to uncover the hidden structure in a data set.15 As an unsupervised machine learning method, it requires researchers to label the individual groups or clusters based on data interpretation.15 Among the various methods of cluster analysis, hierarchical cluster analysis was selected for this study as it provides an intuitive way to examine internal relationships between groups (or clusters) beyond a non-nested clustering output.16A dendrogram, which is visualized hierarchical clustering presented as a binary tree structure of partition, can provide a systematic understanding of a given data set.16 To cluster the abstracts (n = 506) within similar groups, the “textmineR” R package17 was used. Specifically, agglomerative hierarchical clustering using Ward’s method14 was selected for the merge rule.
Predictive analytics and machine learning in stroke and neurovascular medicine
Published in Neurological Research, 2019
Hamidreza Saber, Melek Somai, Gary B. Rajah, Fabien Scalzo, David S. Liebeskind
The two major unsupervised learning methods are clustering and principal component analysis (PCA). Clustering groups subject with similar traits together into clusters, without any outcome information. Popular clustering algorithms include k-means clustering, hierarchical clustering and Gaussian mixture clustering [6]. These algorithms are especially useful when the trait of interest is extracted from multi-dimensionsional datasets, such as the number of genes in a genome-wide association study. For example, unsupervised machine learning has been applied to extract embedded and latent features from EHR data [7]. In addition, unsupervised learning can be applied to explore new applications such as disease characterization for heterogeneous disorders (where the etiology is devised according to the various pathophysiologic mechanisms), which could in turn provide new paths to therapy.
Protein–protein interaction networks and different clustering analysis in Burkitt’s lymphoma
Published in Hematology, 2018
Cun Liu, Lijuan Liu, Chao Zhou, Jing Zhuang, Lu Wang, Yue Sun, Changgang Sun
Clustering analysis, which is based on a certain algorithm and a given rule mining the protein network dense subgraph is a type of important method of mining complex data [20].Traditional clustering algorithms are divided into hierarchical-based methods, graph-based methods, density-based methods and so on [21]. However, there is no reliable evidence that the results of these methods have significant biological implications. Different types of clustering algorithms have their advantages and disadvantages: the biggest advantage of graph-based methods is simple and efficient, the advantage of the hierarchical-based method is that it can make the hierarchical modules of the whole protein network to be presented in a tree structure. However, the deficiency of the above two methods is that each protein node can only belong to one cluster. The density-based method allows a protein to repeatedly emerge in the process of expanding the search, which enables achieving the goal that the same protein belongs to different clusters, but this method cannot identify the non-dense sub-graph structure in the protein network [22]. In the actual protein network, each protein node may belong to a number of protein complexes or functional modules, with multiple functions, involved in a number of different biological processes [23].
Related Knowledge Centers
- Anomaly Detection
- Balance Theory
- Biclustering
- Bioinformatics
- Dendrogram
- Neural Network
- Upgma
- Empirical Distribution Function
- Personality Psychology
- Determining The Number of Clusters In A Data Set