Cluster analysis

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Cluster analysis

A statistical technique that identifies clusters of stocks whose returns are highly correlated within each cluster and relatively uncorrelated across clusters. Cluster analysis has identified groupings such as growth, cyclical, stable, and energy stocks.
References in periodicals archive ?
Through color data clustering, the K-means clustering method is employed to cluster color data and the centroids of the clusters are used to construct a color palette.
Clustering for clutter removal differs from normal data clustering. Instead of finding real clusters, we aim at grouping data merely for visual clarity and better computer-human interaction.
The service collects plant operation data and analyzes them by means of the adaptive resonance theory (ART), one of AI-based data clustering technologies, to swiftly detect abnormalities in devices and equipment that constitute the plant arising from multiple causes that are difficult to be discovered by predictive maintenance systems based on ordinary prediction models or by human judgment.
Jain, "Data clustering: 50 years beyond K-means," Pattern Recognition Letters, vol.
The K-means method has been a commonly used data clustering for unsupervised learning tasks, forming groups from the centroid distance using similarity measure based on the City Block distance metric (Equation 3), optimizing the function of the squared error (23).
introduces the concept of ego of data and implements two steps of data clustering for the IoTs, which obscures the specific location information and achieves the anonymization protection.
In [12], fuzzy double c-means performed clustering well with different datasets on the data clustering and image segmentation.
Let us mention several of the most popular among them: density-based clustering methods [32], grid-based clustering methods [33], model-based clustering methods [34], categorical or mixed data clustering methods [35, 36], fuzzy clustering methods [37], and others.
In the process of data clustering, the clustering algorithm can automatically divide data points into different sets according to the attributes.
Second, it provides a taxonomy [81]that highlights some very important aspects in the context of evolutionary data clustering. The paper ends by addressing some significant concerns and open questions that can be subject of future research.
Intelligent Multidimensional Data Clustering and Analysis
In this work we are presenting the extension of the FPCM using Type-2 Fuzzy Logic Techniques to provide this method with the capability of handling a higher degree of uncertainty in a dataset to solve real world problems where data clustering is involved.