Complete Gradient Clustering Algorithm for Features Analysis of X-Ray Images

Charytanowicz, Małgorzata; Niewczas, J.; Kulczycki, Piotr; Kowalski, Piotr A.; Łukasik, Szymon; Żak, Sławomir

doi:10.1007/978-3-642-13105-9_2

Cited by 148 publications

(83 citation statements)

References 7 publications

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“…were used by taken from UCI database [11]. Basic characteristics of these data sets are that intraclass scatters (standard deviations) of data sets have gradually changing rates and include gradually changing amount of data belonging to different classes which are at the same or very close position intraclass standard deviation values of data sets used in the study are shown in Table 1.…”

Section: Methodsmentioning

confidence: 99%

Density Weighted K-Nearest Neighbors Algorithm for Outliers in the Training Set Are So Close to the Test Element

Akben¹,

Alkan²

2015

JEE

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Abstract:In KNN (K-Nearest Neighbour) method, the distance-weighted algorithm is applied in order to reduce the effect of noisy data. However, as this weighting algorithm will be insufficient when the outliers in the training set are so close to the test element, a new weighting algorithm is required for KNN. In this study, instead of distance-weighting, a new density-weighted KNN algorithm is proposed for reducing the effect of noisy data. In the first stage of the proposed method, the coefficient of density of each element in the training set was obtained by Parzen window method. And then, the membership of each test element was determined according to the total of density coefficients (weights) of neighbours belonging to the same class. As for the last stage, the performance results of the frequently used KNN methods and the proposed method (Density-weighted KNN, Classical KNN and Distance-weighted KNN) were compared. The obtained results have shown that the proposed method is more successful by almost 1% than classical KNN method, by 9% than distance weighted KNN method. Moreover, it is more successful than other KNN methods when the test element is so close to the training set elements.

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Section: Methodsmentioning

confidence: 99%

Density Weighted K-Nearest Neighbors Algorithm for Outliers in the Training Set Are So Close to the Test Element

Akben¹,

Alkan²

2015

JEE

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“…We also considered datasets from the UCI Machine Learning Repository [34][35][36][37] and compare HCDP with DBSCAN and CDP algorithms. For the clustering results, we evaluated them using normalized mutual information (NMI) score, which can be information theoretically interpreted.…”

Section: Complexitymentioning

confidence: 99%

A Novel Hierarchical Clustering Algorithm Based on Density Peaks for Complex Datasets

et al. 2018

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Clustering aims to differentiate objects from different groups (clusters) by similarities or distances between pairs of objects. Numerous clustering algorithms have been proposed to investigate what factors constitute a cluster and how to efficiently find them. The clustering by fast search and find of density peak algorithm is proposed to intuitively determine cluster centers and assign points to corresponding partitions for complex datasets. This method incorporates simple structure due to the noniterative logic and less few parameters; however, the guidelines for parameter selection and center determination are not explicit. To tackle these problems, we propose an improved hierarchical clustering method HCDP aiming to represent the complex structure of the dataset. A k-nearest neighbor strategy is integrated to compute the local density of each point, avoiding to select the nonnecessary global parameter d c and enables cluster smoothing and condensing. In addition, a new clustering evaluation approach is also introduced to extract a "flat" and "optimal" partition solution from the structure by adaptively computing the clustering stability. The proposed approach is conducted on some applications with complex datasets, where the results demonstrate that the novel method outperforms its counterparts to a large extent.

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“…Each instance is defined by the values of 7 features. Seeds data set is first used in [23] and also investigated in [13].…”

Section: A Used Datasetsmentioning

confidence: 99%

Data Distribution Aware Classification Algorithm based on K-Means

Tulgar¹,

Haydar²,

Erşan³

2017

ijacsa

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Complete Gradient Clustering Algorithm for Features Analysis of X-Ray Images

Cited by 148 publications

References 7 publications

Density Weighted K-Nearest Neighbors Algorithm for Outliers in the Training Set Are So Close to the Test Element

Density Weighted K-Nearest Neighbors Algorithm for Outliers in the Training Set Are So Close to the Test Element

A Novel Hierarchical Clustering Algorithm Based on Density Peaks for Complex Datasets

Data Distribution Aware Classification Algorithm based on K-Means

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