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Classification of normalized cluster methods in an order theoretic model
1991
Discrete Applied Mathematics
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., Classification of normalized cluster methods in an order theoretic model, Discrete Applied Mathematics 32 (1991) l-29. ...
An interesting class of cluster methods in the Janowitz model for cluster analysis is explored. Normalized cluster methods, those which produce splitting levels between 0 and 1 inclusive, are de- ...
In Janowitz presented an order theoretic model for cluster analysis based on the Jardine-Sibson model. ...
doi:10.1016/0166-218x(91)90021-n
fatcat:okgiep7jzbb7vfdaprhrwqzubm
Revitalizing the typological approach: Some methods for finding types
2017
Journal for Person-Oriented Research
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An artificial data set and an empirical data set were analyzed using two different methodological approaches, one more explorative (using LICUR, a cluster analysis-based procedure) and one more model-based ...
It was also argued that, in a number of situations, a more explorative approach could be more useful than a standard model-based one. ...
Acknowledgment The preparation of the present article was supported by the National Research, Development and Innovation Office of Hungary (Grant No. K 116965). ...
doi:10.17505/jpor.2017.04
pmid:33569123
pmcid:PMC7869617
fatcat:v42kzslrpzbxfa47zoswgge7q4
A Graph-Based Framework for Web Document Mining
[chapter]
2004
Lecture Notes in Computer Science
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In this paper we describe methods of performing data mining on web documents, where the web document content is represented by graphs. ...
We show how traditional clustering and classification methods, which usually operate on vector representations of data, can be extended to work with graph-based data. ...
The 5-simple distance representation was an improvement in 9 out of 12 cases. Raw frequency was better in 8 of 12 cases, while normalized frequency was an improvement in 11 of 12 cases. ...
doi:10.1007/978-3-540-28640-0_38
fatcat:2oubhzxixrgzrncps7twnaxzwu
Multi-Scale Normalization Method Combined With a Deep CNN Diagnosis Model of Dynamometer Card in SRP Well
2022
Frontiers in Earth Science
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In order to improve the accuracy and recall rate of multi-condition diagnosis of SRP well and solve the problem of inseparable data attributes caused by traditional dynamometer card normalization methods ...
In addition, the preprocessing method of dynamometer card proposed is applicable to all deep learning models and machine learning models. ...
this paper, and PC is the code contributor of this research and the main writer of this paper. ZF provided guidance for the writing of this paper. ...
doi:10.3389/feart.2022.852633
fatcat:pueqgdxpdnhobbzfnufjxgnz7i
Page 6593 of Mathematical Reviews Vol. , Issue 90K
[page]
1990
Mathematical Reviews
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The authors realised that the methods fit naturally both into the theory of probabilistic metric spaces and into a slightly generalised version of the order-theoretic model, and the purpose of their paper ...
An application of this notion to the stress-strength model in reliability, considered by Ebrahimi (1985), is given. ...
The full Bayesian significance test for mixture models: results in gene expression clustering
2008
Genetics and Molecular Research
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Compared to Mclust (model-based clustering), our method shows more consistent results. ...
de Aperfeiçoamento de Pessoal de Nível Superior, CNPq -Conselho Nacional de Desenvolvimento Científico e Tecnológico, FAPESP -Fundação de Amparo à Pesquisa do Estado de São Paulo, and the University of ...
In this study, we propose a clustering method based on normal mixture models, where the number of clusters is decided by sequential significance tests. ...
doi:10.4238/vol7-3x-meeting06
fatcat:je643afcpbh3nmuxylmo5xp3cy
Toward the integration of classification theory and methods
1981
Journal of Abnormal Psychology
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of psychopathology that were derived using empirical clustering methods, and (c) classifications of behavioral disorders that have an explicit theoretical basis. ...
Although various criteria have been proposed for the evaluation of psychiatric classifications, there is need for an integrated paradigm. ...
Classifications Derived Using Clustering Methods In 1966, Lorr presented a series of studies in typing psychotic patients. ...
doi:10.1037/0021-843x.90.1.68
fatcat:ec37yeww4vfolkpvvb2u2makw4
A New Methodology for Automatic Cluster-Based Kriging Using K-Nearest Neighbor and Genetic Algorithms
2019
Information
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The conventional method of variogram modelling consists of using specialized knowledge and in-depth study to determine which parameters are suitable for the theoretical variogram. ...
The modelling of the variogram is an essential step of the kriging process because it drives the accuracy of the interpolation model. ...
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations The following abbreviations are used in this manuscript: ...
doi:10.3390/info10110357
fatcat:lcmnpfq3nnh57fveqrlq4uoasy
The Noise Component in Model-based Cluster Analysis
[chapter]
2008
Studies in Classification, Data Analysis, and Knowledge Organization
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The so-called noise-component has been introduced by Banfield and Raftery (1993) to improve the robustness of cluster analysis based on the normal mixture model. ...
The first one consists of replacing the uniform distribution by a fixed constant, modelling an improper uniform distribution that doesn't depend on the data. ...
Banfield and Raftery (1993) introduced the term "model-based cluster analysis" for such methods. ...
doi:10.1007/978-3-540-78246-9_16
fatcat:d3beuyzxqjfsbcw6ovxsi6czry
Parameter Resolution of the Estimation Methods for Power Law Indices
2021
Discrete Dynamics in Nature and Society
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The accuracy of parameter estimation plays an important role in economic and social models and experiments. ...
Last, we select an algorithm with finer resolution to estimate the Pareto index for the Forbes list of global rich data in recent 10 years and analyze the changes in the gap between the rich and the poor ...
In the second step, to divide the elements in θ into two groups, one can use clustering [9, 10] , which is an unsupervised learning method. ...
doi:10.1155/2021/5593959
fatcat:77s5pnsd5rebtlvixskfq6p4gm
Stability-Based Model Order Selection in Clustering with Applications to Gene Expression Data
[chapter]
2002
Lecture Notes in Computer Science
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We present an empirical estimator for the theoretically derived stability index, based on resampling. ...
The concept of cluster stability is introduced to assess the validity of data partitionings found by clustering algorithms. ...
Conclusions We have introduced the concept of cluster stability as a means for solving the model order selection problem in unsupervised clustering. ...
doi:10.1007/3-540-46084-5_99
fatcat:qrgyh6zedbcp7pnyhbvvqottd4
Classification of Meteorological Satellite Ground System Applications
2017
Atmospheric and Climate Sciences
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In order to classify these applications according to the resource consumption and improve the rational allocation of system resources, this paper introduces several application analysis algorithms. ...
Firstly, the requirements are abstractly described, and then analyzed by hierarchical clustering algorithm. Finally, the benchmark analysis of resource consumption is given. ...
Acknowledgments The work presented in this study is supported by National High-tech R&D Program (2011AA12A104). ...
doi:10.4236/acs.2017.73028
fatcat:b66yfoj3vzecfd6j6d4jhfytly
Estimating the Expected Effectiveness of Text Classification Solutions under Subclass Distribution Shifts
2012
2012 IEEE 12th International Conference on Data Mining
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To estimate the expected effectiveness of a classification solution we partition a test sample by means of clustering. ...
We show that the effectiveness is normally distributed and introduce a probabilistic lower bound that is used for model selection. ...
We define m as tuple (α, h) in order to emphasize the fact that each classification solution has an underlying design process. ...
doi:10.1109/icdm.2012.89
dblp:conf/icdm/LipkaSS12
fatcat:uodau3fiqnhzbgxu4w6ipmj7py
Imbalanced Data Set CSVM Classification Method Based on Cluster Boundary Sampling
2016
Mathematical Problems in Engineering
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This paper creatively proposes a cluster boundary sampling method based on density clustering to solve the problem of resampling in IDS classification and verify its effectiveness experimentally. ...
We use the clustering density threshold and the boundary density threshold to determine the cluster boundaries, in order to guide the process of resampling more scientifically and accurately. ...
Acknowledgments This paper is partially supported by Natural Science Foundation of Province (QC2013C060), Science Funds for the Young Innovative Talents of HUST (no. 201304), China Postdoctoral Science ...
doi:10.1155/2016/1540628
fatcat:3tniqpfgrnfc7kfgtgjbi4ulcu
Special issue on "Advances on model-based clustering and classification"
2019
Advances in Data Analysis and Classification
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This Special Issue of ADAC is devoted to recent developments in Model-Based Clustering and Classification which is an increasingly active area in both theoretical and applied research. ...
This can provide an opportunity for cheap, wide and meaningful enlarging of "classical" model families. ...
Luis Benites an extension of this model is proposed by considering scale mixtures of normal distributions. ...
doi:10.1007/s11634-019-00355-w
fatcat:qz4nw4vd5ralnbffs4qvb3p5ja
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