Estimating the Optimal Number of Clusters Via Internal Validity Index
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DOI: 10.1007/s11063-021-10427-8
Abstract: Estimating the optimal number of clusters (NC) is pivotal in cluster analysis. From the viewpoint of sample geometry, a novel internal clustering validity index, which is termed the between-within cluster (BWC) index, is designed in… read more here.
Keywords: data sets; number clusters; index; optimal number ... See more keywords
Automatically finding the number of clusters based on simulated annealing
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DOI: 10.1007/s12204-017-1813-9
Abstract: Based on simulated annealing (SA), automatically finding the number of clusters (AFNC) is proposed in this paper to determine the number of clusters and their initial centers. It is a simple and automatic method that… read more here.
Keywords: number clusters; mountain; based simulated; automatically finding ... See more keywords
Estimating the Optimal Number of Clusters k in a Dataset Using Data Depth
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DOI: 10.1007/s41019-019-0091-y
Abstract: This paper proposes a new method called depth difference (DeD), for estimating the optimal number of clusters (k) in a dataset based on data depth. The DeD method estimates the k parameter before actual clustering… read more here.
Keywords: optimal number; depth; number clusters; estimating optimal ... See more keywords
A data-driven selection of the number of clusters in the Dirichlet allocation model via Bayesian mixture modelling
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Statistical Computation and Simulation"
DOI: 10.1080/00949655.2019.1643345
Abstract: ABSTRACT In this paper, we consider a Bayesian mixture model that allows us to integrate out the weights of the mixture in order to obtain a procedure in which the number of clusters is an… read more here.
Keywords: number clusters; bayesian mixture; data driven; model ... See more keywords
Cluster randomized trials with a small number of clusters: which analyses should be used?
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DOI: 10.1093/ije/dyy057
Abstract: Background: Cluster randomised trials (CRTs) are increasingly used to assess the effectiveness of health interventions. Three main analysis approaches are: cluster-level analyses, mixed-models and generalized estimating equations (GEEs). Mixed models and GEEs can lead to… read more here.
Keywords: number clusters; cluster level; small number; cluster ... See more keywords
Nonnegative Matrix Factorization Based Consensus for Clusterings With a Variable Number of Clusters
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DOI: 10.1109/access.2018.2874038
Abstract: Consensus clustering is an aggregation of base clusterings into an ensemble clustering which is better than the individual base clusterings. It is beneficial to determine the clusters from heterogeneous data. This paper presents a new… read more here.
Keywords: number clusters; base clusterings; consensus; base ... See more keywords
A Fully-Unsupervised Possibilistic C-Means Clustering Algorithm
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DOI: 10.1109/access.2018.2884956
Abstract: In 1993, Krishnapuram and Keller first proposed possibilistic C-means (PCM) clustering by relaxing the constraint in fuzzy C-means of which memberships for a data point across classes sum to 1. The PCM algorithm tends to… read more here.
Keywords: number clusters; algorithm; possibilistic means; pcm algorithm ... See more keywords
Automatic Fuzzy Clustering Using Non-Dominated Sorting Particle Swarm Optimization Algorithm for Categorical Data
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DOI: 10.1109/access.2019.2927593
Abstract: Categorical data clustering has been attracted a lot of attention recently due to its necessary in the real-world applications. Many clustering methods have been proposed for categorical data. However, most of the existing algorithms require… read more here.
Keywords: fuzzy clustering; automatic fuzzy; categorical data; number clusters ... See more keywords
A Centroid Auto-Fused Hierarchical Fuzzy c-Means Clustering
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DOI: 10.1109/tfuzz.2020.2991306
Abstract: Like k-means and Gaussian mixture model (GMM), fuzzy c-means (FCM) with soft partition has also become a popular clustering algorithm and still is extensively studied. However, these algorithms and their variants still suffer from some… read more here.
Keywords: number clusters; number; fuzzy means; caf hfcm ... See more keywords
Subspace Clustering of Categorical and Numerical Data With an Unknown Number of Clusters
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DOI: 10.1109/tnnls.2017.2728138
Abstract: In clustering analysis, data attributes may have different contributions to the detection of various clusters. To solve this problem, the subspace clustering technique has been developed, which aims at grouping the data objects into clusters… read more here.
Keywords: number clusters; subspace clustering; numerical categorical; subspace ... See more keywords
A thermodynamic approach to selecting a number of clusters based on topic modeling
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DOI: 10.1134/s1063785017060207
Abstract: A thermodynamic approach has been applied to solving the problem of selecting the number of clusters/topics in topic modeling. The main principles of this approach are formulated and the behavior of topic models during temperature… read more here.
Keywords: number clusters; selecting number; number; thermodynamic approach ... See more keywords