A New Validity Index for Fuzzy-Possibilistic C-Means Clustering
- URL: http://arxiv.org/abs/2005.09162v1
- Date: Tue, 19 May 2020 01:48:13 GMT
- Title: A New Validity Index for Fuzzy-Possibilistic C-Means Clustering
- Authors: Mohammad Hossein Fazel Zarandi, Shahabeddin Sotudian, Oscar Castillo
- Abstract summary: Fuzzy-Possibilistic (FP) index works well in the presence of clusters that vary in shape and density.
FPCM requires a priori selection of the degree of fuzziness and the degree of typicality.
- Score: 6.174448419090291
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In some complicated datasets, due to the presence of noisy data points and
outliers, cluster validity indices can give conflicting results in determining
the optimal number of clusters. This paper presents a new validity index for
fuzzy-possibilistic c-means clustering called Fuzzy-Possibilistic (FP) index,
which works well in the presence of clusters that vary in shape and density.
Moreover, FPCM like most of the clustering algorithms is susceptible to some
initial parameters. In this regard, in addition to the number of clusters, FPCM
requires a priori selection of the degree of fuzziness and the degree of
typicality. Therefore, we presented an efficient procedure for determining
their optimal values. The proposed approach has been evaluated using several
synthetic and real-world datasets. Final computational results demonstrate the
capabilities and reliability of the proposed approach compared with several
well-known fuzzy validity indices in the literature. Furthermore, to clarify
the ability of the proposed method in real applications, the proposed method is
implemented in microarray gene expression data clustering and medical image
segmentation.
Related papers
- Fuzzy K-Means Clustering without Cluster Centroids [21.256564324236333]
Fuzzy K-Means clustering is a critical technique in unsupervised data analysis.
This paper proposes a novel Fuzzy textitK-Means clustering algorithm that entirely eliminates the reliance on cluster centroids.
arXiv Detail & Related papers (2024-04-07T12:25:03Z) - Rethinking Clustered Federated Learning in NOMA Enhanced Wireless
Networks [60.09912912343705]
This study explores the benefits of integrating the novel clustered federated learning (CFL) approach with non-independent and identically distributed (non-IID) datasets.
A detailed theoretical analysis of the generalization gap that measures the degree of non-IID in the data distribution is presented.
Solutions to address the challenges posed by non-IID conditions are proposed with the analysis of the properties.
arXiv Detail & Related papers (2024-03-05T17:49:09Z) - Minimally Supervised Learning using Topological Projections in
Self-Organizing Maps [55.31182147885694]
We introduce a semi-supervised learning approach based on topological projections in self-organizing maps (SOMs)
Our proposed method first trains SOMs on unlabeled data and then a minimal number of available labeled data points are assigned to key best matching units (BMU)
Our results indicate that the proposed minimally supervised model significantly outperforms traditional regression techniques.
arXiv Detail & Related papers (2024-01-12T22:51:48Z) - Superclustering by finding statistically significant separable groups of
optimal gaussian clusters [0.0]
The paper presents the algorithm for clustering a dataset by grouping the optimal, from the point of view of the BIC criterion.
An essential advantage of the algorithm is its ability to predict correct supercluster for new data based on already trained clusterer.
arXiv Detail & Related papers (2023-09-05T23:49:46Z) - A novel cluster internal evaluation index based on hyper-balls [11.048887848164268]
It is crucial to evaluate the quality and determine the optimal number of clusters in cluster analysis.
In this paper, the multi-granularity characterization of the data set is carried out to obtain the hyper-balls.
The cluster internal evaluation index based on hyper-balls(HCVI) is defined.
arXiv Detail & Related papers (2022-12-30T02:56:40Z) - A One-shot Framework for Distributed Clustered Learning in Heterogeneous
Environments [54.172993875654015]
The paper proposes a family of communication efficient methods for distributed learning in heterogeneous environments.
One-shot approach, based on local computations at the users and a clustering based aggregation step at the server is shown to provide strong learning guarantees.
For strongly convex problems it is shown that, as long as the number of data points per user is above a threshold, the proposed approach achieves order-optimal mean-squared error rates in terms of the sample size.
arXiv Detail & Related papers (2022-09-22T09:04:10Z) - Determinantal consensus clustering [77.34726150561087]
We propose the use of determinantal point processes or DPP for the random restart of clustering algorithms.
DPPs favor diversity of the center points within subsets.
We show through simulations that, contrary to DPP, this technique fails both to ensure diversity, and to obtain a good coverage of all data facets.
arXiv Detail & Related papers (2021-02-07T23:48:24Z) - Scalable Hierarchical Agglomerative Clustering [65.66407726145619]
Existing scalable hierarchical clustering methods sacrifice quality for speed.
We present a scalable, agglomerative method for hierarchical clustering that does not sacrifice quality and scales to billions of data points.
arXiv Detail & Related papers (2020-10-22T15:58:35Z) - A Centroid Auto-Fused Hierarchical Fuzzy c-Means Clustering [30.709797128259236]
Centroid Auto-Fused Hierarchical Fuzzy c-means method (CAF-HFCM)
We present a Centroid Auto-Fused Hierarchical Fuzzy c-means method (CAF-HFCM) whose optimization procedure can automatically agglomerate to form a cluster hierarchy.
Our proposed CAF-HFCM method is able to be straightforwardly extended to various variants of FCM.
arXiv Detail & Related papers (2020-04-27T12:59:22Z) - Clustering Binary Data by Application of Combinatorial Optimization
Heuristics [52.77024349608834]
We study clustering methods for binary data, first defining aggregation criteria that measure the compactness of clusters.
Five new and original methods are introduced, using neighborhoods and population behavior optimization metaheuristics.
From a set of 16 data tables generated by a quasi-Monte Carlo experiment, a comparison is performed for one of the aggregations using L1 dissimilarity, with hierarchical clustering, and a version of k-means: partitioning around medoids or PAM.
arXiv Detail & Related papers (2020-01-06T23:33:31Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.