Performance and Convergence Analysis of Modified C-Means Using Jeffreys-Divergence for Clustering

Seal, Ayan; Karlekar, Aditya; Krejcar, Ondrej; Herrera‐Viedma, Enrique

doi:10.9781/ijimai.2021.04.009

Cited by 8 publications

(4 citation statements)

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“…For example, Karlekar et al [17] used non-linear s-distance for FCM, and Sharma et al [18] proposed an enhanced spectral clustering algorithm by using s-distance. Seal et al [19] first used Jeffreys-divergence measure for FCM, and Seal et al [20] then gave the performance and convergence analysis of modified c-means using Jeffreys-divergence measure. Garain and Das [21] modified k-means algorithm to be the so-called K-RMS algorithm to increase accuracy with less number of iterations.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Multi-View K-Means Clustering Algorithm

Yang

Hussain

2023

IEEE Access

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Since advanced technologies via social media, internet, virtual communities and networks and internet of things (IoT), there are more multi-view data to be collected. Multi-view clustering is a substantial tool as a natural design for clustering multi-view data. K-means (KM) clustering for (singleview) data had been extended for handling multi-view data, called multi-view KM (MV-KM). In the literature, most MV-KM clustering algorithms are reported to be influenced by initializations and also need a given number of clusters. In this paper, we propose an unsupervised type of MV-KM clustering algorithm so that it can automatically find an optimal number of clusters without any initialization. We call it unsupervised MV-KM (U-MV-KM). Moreover, we also propose three multi-view cluster validity indices, called multi-view Dunn index (MV-Dunn), multi-view generalized Dunn index (MV-G-Dunn) and multiview modified Dunn (MV-M-Dunn) indices for MV-KM clustering algorithms. We make experiments on some synthetic and real data sets and also make comparisons with some existing algorithms. Based on the experimental results and comparisons, the proposed U-MV-KM clustering algorithm actually shows good results. We also apply U-MV-KM to real data sets, the results demonstrate the superiority and usefulness of the U-MV-KM algorithm for clustering multi-view data. INDEX TERMSClustering, K-means (KM), Multi-view k-means (MV-KM), Number of clusters, Initializations, Unsupervised learning schema, Unsupervised MV-KM (U-MV-KM)

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Section: Introductionmentioning

confidence: 99%

Unsupervised Multi-View K-Means Clustering Algorithm

Yang

Hussain

2023

IEEE Access

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“…Clustering techniques are required to unravel important hidden facts and understand the massive data. In addition, clustering could also be used in particular domains, such as gene expression profiles, where domain experts often provide incomplete knowledge in the form of pairwise constraints [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

“…As an unsupervised learning technique, clustering is widely used to explore the structure of a given dataset [1][2][3]. Due to the growth of the Internet of Things, data is generated every day across the globe [2].…”

Section: Introductionmentioning

confidence: 99%

A Novel Clustering Method Based on Adjacent Grids Searching

Li,

Zhong,

Liao

et al. 2023

Entropy

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Clustering is used to analyze the intrinsic structure of a dataset based on the similarity of datapoints. Its widespread use, from image segmentation to object recognition and information retrieval, requires great robustness in the clustering process. In this paper, a novel clustering method based on adjacent grid searching (CAGS) is proposed. The CAGS consists of two steps: a strategy based on adaptive grid-space construction and a clustering strategy based on adjacent grid searching. In the first step, a multidimensional grid space is constructed to provide a quantization structure of the input dataset. The noise and cluster halo are automatically distinguished according to grid density. Moreover, the adaptive grid generating process solves the common problem of grid clustering, in which the number of cells increases sharply with the dimension. In the second step, a two-stage traversal process is conducted to accomplish the cluster recognition. The cluster cores with arbitrary shapes can be found by concealing the halo points. As a result, the number of clusters will be easily identified by CAGS. Therefore, CAGS has the potential to be widely used for clustering datasets with different characteristics. We test the clustering performance of CAGS through six different types of datasets: dataset with noise, large-scale dataset, high-dimensional dataset, dataset with arbitrary shapes, dataset with large differences in density between classes, and dataset with high overlap between classes. Experimental results show that CAGS, which performed best on 10 out of 11 tests, outperforms the state-of-the-art clustering methods in all the above datasets.

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“…In addition to the possible applications mentioned above that can utilize the K-means clustering algorithm, theoretical studies have recently been proposed to improve the clustering algorithm itself based on improved distance metrics, such as adaptive fused distance [7] and S-distance [8]. Furthermore, instead of relying on a simple similarity measure (such as Euclidean distance) to divide the dataset into different groups, a study of an advanced similarity measure to identify hidden patterns in data based on Jeffreydifferences was proposed in [9,10].…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Distance Computing: Application to K-Means Clustering

et al. 2022

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Distance computation between two input vectors is a widely used computing unit in several pattern recognition, signal processing and neuromorphic applications. However, the implementation of such a functionality in conventional CMOS design requires expensive hardware and involves significant power consumption. Even power-efficient current-mode analog designs have proved to be slower and vulnerable to variations. In this paper, we propose an approximate mixed-signal design for the distance computing core by noting the fact that a vast majority of the signal processing applications involving this operation are resilient to small approximations in the distance computation. The proposed mixed-signal design is able to interface with external digital CMOS logic and simultaneously exhibit fast operating speeds. Another important feature of the proposed design is that the computing core is able to compute two variants of the distance metric, namely the (i) Euclidean distance squared (L22 norm) and (ii) Manhattan distance (L1 norm). The performance of the proposed design was evaluated on a standard K-means clustering algorithm on the “Iris flower dataset”. The results indicate a throughput of 6 ns per classification and ∼2.3× lower energy consumption in comparison to a synthesized digital CMOS design in commercial 45 nm CMOS technology.

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Performance and Convergence Analysis of Modified C-Means Using Jeffreys-Divergence for Clustering

Cited by 8 publications

References 0 publications

Unsupervised Multi-View K-Means Clustering Algorithm

Unsupervised Multi-View K-Means Clustering Algorithm

A Novel Clustering Method Based on Adjacent Grids Searching

Energy Efficient Distance Computing: Application to K-Means Clustering

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