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Breast cancer is the second largest cause of cancer deaths among women. At the same time, it is also among the most curable cancer types if it can be diagnosed early.

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group called a cluster are more similar in some sense to each other than to those in other groups clusters. It is a main task of exploratory data mining , and a common technique for statistical data analysis , used in many fields, including pattern recognition , image analysis , information retrieval , bioinformatics , data compression , computer graphics and machine learning. Cluster analysis itself is not one specific algorithm , but the general task to be solved.

Text Segmentation in Web Images Using Color Perception and Topological Features

For the shortcoming of fuzzy c -means algorithm FCM needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rule and obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process.

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Data clustering: a review

Clustering is the process of partitioning elements into a number of groups clusters such that elements in the same cluster are more similar than elements in different clusters. Clustering has been applied in a wide variety of fields, ranging from medical sciences, economics, computer sciences, engineering, social sciences, to earth sciences [1,2], reflecting its important role in scientific research. With several hundred clustering methods in existence [3], there is clearly no shortage of clustering algorithms but, at the same time, satisfactory answers to some basic questions are still to come. Clustering methods are nowadays essential tools for the analysis of gene expression data, becoming routinely used in many research projects [4]. Many papers have shown that genes or proteins of similar function cluster together [], and clustering methods have been used to solve many problems of biological nature. One of the most interesting of these problems is related to disease subtyping , i.


PDF | The adoption of triangular fuzzy sets to define Strong Fuzzy Partitions (​points of separation between cluster projections on eration of a well-formed triangular fuzzy set (red In IEEE, editor, 18th International Conference or compactness–separability, do not allow to find the optimal partition.


Comparison of Fuzzy Clustering Methods and Their Applications to Geophysics Data

To organize the wide variety of data sets automatically and acquire accurate classification, this paper presents a modified fuzzy c -means algorithm SP-FCM based on particle swarm optimization PSO and shadowed sets to perform feature clustering. SP-FCM introduces the global search property of PSO to deal with the problem of premature convergence of conventional fuzzy clustering, utilizes vagueness balance property of shadowed sets to handle overlapping among clusters, and models uncertainty in class boundaries. This new method uses Xie-Beni index as cluster validity and automatically finds the optimal cluster number within a specific range with cluster partitions that provide compact and well-separated clusters.

Motivation: Fuzzy c-means clustering is widely used to identify cluster structures in high-dimensional datasets, such as those obtained in DNA microarray and quantitative proteomics experiments. One of its main limitations is the lack of a computationally fast method to set optimal values of algorithm parameters. Wrong parameter values may either lead to the inclusion of purely random fluctuations in the results or ignore potentially important data. The optimal solution has parameter values for which the clustering does not yield any results for a purely random dataset but which detects cluster formation with maximum resolution on the edge of randomness.

Cluster analysis

The fuzzy clustering algorithm has been widely used in the research area and production and life. However, the conventional fuzzy algorithms have a disadvantage of high computational complexity. This article proposes an improved fuzzy C-means FCM algorithm based on K-means and principle of granularity. This algorithm is aiming at solving the problems of optimal number of clusters and sensitivity to the data initialization in the conventional FCM methods. The initialization stage of the K-medoid cluster, which is different from others, has a strong representation and is capable of detecting data with different sizes.

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Improved FCM Algorithm Based on K-Means and Granular Computing

3 Comments

Virginie P. 11.05.2021 at 23:24

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Gustave B. 12.05.2021 at 04:56

It includes contributions from diverse areas of soft computing such as uncertain computation, Z-information processing, neuro-fuzzy approaches, evolutionary computing and others.

Dennis A. 18.05.2021 at 09:45

David J.

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