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Development And Evaluation Of Kernel-Based Clustering Validity Indices
2012
Zenodo
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Publication in the conference proceedings of EUSIPCO, Bucharest, Romania, 2012 ...
However, to our best knowledge, the kernel-based clustering validity indices have not been investigated. It motivates us to develop and evaluate the kernel-based clustering validity indices. ...
The rest of this paper is organized as follows: Sec. 2 reviews the validity indices and kernel method, Sec. 3 develops the kernel validity indices based on the conventional ones and Sec. 4 presents the ...
doi:10.5281/zenodo.42846
fatcat:hbvridhdabdojkei4ftbuctinm
Anomaly based Intrusion Detection using Modified Fuzzy Clustering
2017
International Journal of Interactive Multimedia and Artificial Intelligence
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Evaluation using Cluster Validity Indices In this section we evalated performance of the propsoed method using four cluster validity indices. ...
The performance of the proposed method is evaluated using four cluster validity indices, accuracy and false positive rate. ...
doi:10.9781/ijimai.2017.05.002
fatcat:cbapttn5wzeqrkhuljzic6ci6a
Automatic Kernel Size Determination for Deep Neural Networks Based Hyperspectral Image Classification
2018
Remote Sensing
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The evaluation indicators of the clustering results can be divided into two categories, those for the samples without labels and those for the samples with given labels [30] [31] [32] [33] . ...
In the other category for non-labeled data, the indicators, such as Cluster Accuracy (CA), Rand Index (RI) and Normalized Mutual Information (NMI), are used for evaluating clustering results. ...
The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results. ...
doi:10.3390/rs10030415
fatcat:2hhl7pbclrf73koq2fs2euyd4a
MKF-Firefly: Hybridization of Firefly and Multiple Kernel-Based Fuzzy C-Means Algorithm
2016
IJARCCE
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This paper presents a new clustering algorithm, namely, MKF-Firefly which is developed by combining the multiple kernel-based objective function and firefly algorithm. ...
The proposed clustering process is evaluated using Rand coefficient, Jaccard coefficient and Clustering Accuracy on the two different datasets like iris and wine. ...
After the development of FCM, various clustering algorithms have been developed in the literature by including the kernel function and various theories like, rough set and so on. ...
doi:10.17148/ijarcce.2016.5742
fatcat:4yph4p35wbgbhesdlf6xztmhmi
Does deep learning help topic extraction? A kernel k-means clustering method with word embedding
2018
Journal of Informetrics
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An empirical study on bibliometric topic extraction from articles published by three top-tier bibliometric journals between 2000 and 2017, supported by expert knowledge-based evaluations, provides supplemental ...
The experimental results of a comparison of this method with four clustering baselines (i.e., k-means, fuzzy c-means, principal component analysis, and topic models) on two bibliometric datasets demonstrate ...
Acknowledgements We acknowledge Arho Suominen and Ying Huang for their efforts in the pre-round expert knowledge-based evaluation, and our heartfelt appreciation goes to Lutz Bornmann, Kevin Boyack, Andrea ...
doi:10.1016/j.joi.2018.09.004
fatcat:t75tldepafcxrptzk5s2jc26ki
Kernel Based Fuzzy Ant Clustering with Partition Validity
2006
2006 IEEE International Conference on Fuzzy Systems
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The partition validity metric was developed based on the kernelized distance measure. ...
The swarm based algorithm determines the number of cluster centers of the input data by using a modification to the fuzzy cluster validity metric proposed by Xie and Beni. ...
In Section 3, we discuss ant based clustering using partition validity to evaluate the goodness of partitions. Section 4 explains the data sets used and the experimental results. ...
doi:10.1109/fuzzy.2006.1681695
dblp:conf/fuzzIEEE/GuH06
fatcat:cpja6rocs5d7df4mwtkdr4icba
Identifying Similarities in Epileptic Patients for Drug Resistance Prediction
[article]
2017
arXiv
pre-print
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of 0.83(+/-0.3) testing using 7 fold cross validation. ...
Observations of test results indicate using a radial basis function kernel PCA to reduce features ingested by a Gradient Boosted Decision Tree Ensemble lead to gains in improved accuracy in mapping a binary ...
Problem Formulation For this study, two hypotheses were developed: Metrics for evaluation for classifiers included AUC and Cross-Validation Accuracy. ...
arXiv:1704.08361v1
fatcat:utielhbur5ertmsckjeg5mcg3m
Unsupervised Clustering-Based Short-Term Solar Forecasting
2018
IEEE Transactions on Sustainable Energy
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The daily GHI time series is first clustered by an Optimized Cross-validated ClUsteRing (OCCUR) method, which determines the optimal number of clusters and best clustering results. ...
This UC-based method consists of three parts: GHI time series unsupervised clustering, pattern recognition, and UC-based forecasting. ...
To validate the developed UC-M3 solar forecasting method, the effectiveness of both UC-based forecasting and M3based forecasting are evaluated by comparing two sets of counterparts, which are M3 models ...
doi:10.1109/tste.2018.2881531
fatcat:7hyi6y3igretpl57ajzll5mfam
An Unsupervised Clustering-Based Short-Term Solar Forecasting Methodology Using Multi-Model Machine Learning Blending
[article]
2018
arXiv
pre-print
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The developed UC-based methodology is validated by using 1-year of data with six solar features. ...
The daily GHI time series is first clustered by an Optimized Cross-validated ClUsteRing (OCCUR) method, which determines the optimal number of clusters and best clustering results. ...
To validate the developed UC-M3 solar forecasting method, the effectiveness of both UC-based forecasting and M3-based forecasting are proven by comparing two sets of counterparts, which are M3 models vs ...
arXiv:1805.04193v1
fatcat:3pohg7tfm5ch7egmsjwuetwzoa
Establishment of the Credit Indicator System of Micro Enterprises Based on Support Vector Machine and R-Type Clustering
2018
Mathematical Problems in Engineering
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) curve reveals that each of the screened credit evaluation indicators is valid. ...
This paper provides a screening model for credit evaluation indicators of micro enterprises and uses credit data of 860 micro enterprises samples in Inner Mongolia in western China for application analysis ...
The Method of the First Round of Indicator Selection Based on SVM (1) The Determination of Kernel Function. ...
doi:10.1155/2018/6390720
fatcat:q6nu6vyjwvaanflrqxubszsszq
Kernel Clustering with Sigmoid-based Regularization for Efficient Segmentation of Sequential Data
[article]
2021
arXiv
pre-print
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Combining it with objective of the balanced kernel clustering, we formulate a differentiable model termed Kernel clustering with sigmoid-based regularization (KCSR), where the gradient-based algorithm ...
Through extensive experiments on various types of data sequences performances of our models are evaluated and compared with those of the existing methods. ...
Objective of the proposed method called Kernel clustering with sigmoid regularization (KCSR) is smooth and can be effectively solved using gradient-based algorithm. • Development of a stochastic variant ...
arXiv:2106.11541v1
fatcat:n76o6g4qarcqjfu7t2k62vuehi
Diagnosis of Autism Spectrum Disorders Using Temporally Distinct Resting-State Functional Connectivity Networks
2016
CNS Neuroscience & Therapeutics
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A Pearson correlation-based functional connectivity network is then constructed for each cluster to allow for better exploration of spatiotemporal dynamics of individual neural activity. ...
A clustering algorithm is first used to temporally decompose R-fMRI time series into distinct clusters with similar spatial distribution of neural activity based on the assumption that functionally distinct ...
Effects of SVM Kernel Types We evaluated the performance of the proposed method when different SVM kernels (linear, radial basis function (RBF), and polynomial) are used. ...
doi:10.1111/cns.12499
pmid:26821773
pmcid:PMC4839002
fatcat:hranpsrv25cunic5v4xllx5mky
Kernel based automatic clustering using modified particle swarm optimization algorithm
2007
Proceedings of the 9th annual conference on Genetic and evolutionary computation - GECCO '07
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This paper introduces a method for clustering complex and linearly non-separable datasets, without any prior knowledge of the number of naturally occurring clusters. ...
Use of the kernel function makes it possible to cluster data that is linearly non-separable in the original input space into homogeneous groups in a transformed highdimensional feature space. ...
The partitions should maintain the following properties:
KERNEL BASED CLUSTERING AND THE VALIDITY INDICES
The Crisp Clustering Problem 1) Φ ≠ i C } ,..., 2 , 1 { k i ∈ ∀ . 2) Φ = ∩ j i C C , j i ≠ ...
doi:10.1145/1276958.1276960
dblp:conf/gecco/AbrahamDK07
fatcat:du4uw3kkrzgm7pt6xlavsrzzwa
Quantile Regression and Clustering Models of Prediction Intervals for Weather Forecasts: A Comparative Study
2019
Forecasting
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They are developed to investigate prediction intervals of weather forecasts using various quantile regression methods as well as cluster-based probabilistic forecasts using fuzzy methods. ...
In this paper, we investigate several learning methods to train and evaluate prediction interval models of weather forecasts. ...
Funding: This research was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC).
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/forecast1010012
fatcat:dhapcswpivekvizfuylcmkouby
Searching Remote Homology with Spectral Clustering with Symmetry in Neighborhood Cluster Kernels
2013
PLoS ONE
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The existing cluster kernel methods based on neighborhoods and profiles and the Markov clustering algorithms are currently the most popular methods for protein family recognition. ...
When combined with the corrections based on modified symmetry based proximity norm deemphasizing outliers, the technique proposed in this article outperforms other state-of-the-art cluster kernels among ...
Quantitative performance evaluation We evaluate the clustering solutions for all kernels objectively by measuring five validity measures Dunn, Davies-Bouldin, Kruskal, Rand and Jaccard indices as defined ...
doi:10.1371/journal.pone.0046468
pmid:23457439
pmcid:PMC3574063
fatcat:gz3mokeurnd65ffaxtw2wbxuam
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