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.
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.
