DENDIS: A new density-based sampling for clustering algorithm

Ros, Frédéric; Guillaume, Serge

doi:10.1016/j.eswa.2016.03.008

Cited by 35 publications

(14 citation statements)

References 32 publications

(16 reference statements)

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“…Several techniques are available for local density estimation based on kernel or neighborhood, either the number of nearest neighbors or the number of neighbors within a given hyper-volume. They are not studied in the work, the reader may refer to Ros & Guillaume (2016, 2017 for a recent survey.…”

Section: Local Density Estimation and Noise Labelingmentioning

confidence: 99%

A hierarchical clustering algorithm and an improvement of the single linkage criterion to deal with noise

Ros

Guillaume

2019

Expert Systems with Applications

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Hierarchical clustering is widely used in data mining. The single linkage criterion is powerful, as it allows for handling various shapes and densities, but it is sensitive to noise. Two improvements are proposed in this work to deal with noise. First, the single linkage criterion takes into account the local density to make sure the distance involve core points of each group. Second, the hierarchical algorithm forbids the merging of representative clusters, higher than a minimum size, once identified. The experiments include a sensitivity analysis to the parameters and a comparison of the available criteria using datasets known in the literature. The latter proved that local criteria yield better results than global ones. Then, the three single linkage criteria were compared in more challenging situations that highlighted the complementariness between the two levels of improvement: the criterion and the clustering algorithm.

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Section: Local Density Estimation and Noise Labelingmentioning

confidence: 99%

A hierarchical clustering algorithm and an improvement of the single linkage criterion to deal with noise

Ros

Guillaume

2019

Expert Systems with Applications

Self Cite

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“…A hashing function is used when doing a biased sampling to map bins in space to a linear ordering. An incremental algorithm is introduced by Frédéric Ros et al [13] to combine distance and density concepts. They manage distance concepts in order to make sure space coverage and fit cluster shapes by selecting representative points in every cluster.…”

Section: Related Workmentioning

confidence: 99%

On the Enhancement of Classification Algorithms Using Biased Samples

Al‐Mamory

2019

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Classification algorithms' performance could be enhanced by selecting many representative points to be included in the training sample. In this paper, a new border and rare biased sampling (BRBS) scheme is proposed by assigning each point in the dataset an importance factor. The importance factor of border points and rare points (i.e. points belong to rare classes) is higher than other points. Then the points are selected to be in the training sample depending on these factors. Including these points in the training sample enhances classifiers experience. The results of experiments on 10 UCI machine learning repository datasets prove that the BRBS algorithm outperforms many sampling algorithms and enhanced the performance of several classification algorithms by about 8%. BRBS is proposed to be easy to configure, covering all points space, and generate a unique samples every time it is executed.

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“…Literature reports many clustering paradigms among which the most important can be categorized into Partitional Clustering [1][2][3][4][5] and its variants [6,7], Hierarchical Clustering [1,3,[8][9][10][11][12], Density-based Clustering [1,3,[13][14][15][16], Grid-based Clustering [1,3,[17][18][19][20], Spectral Clustering [1,3,[21][22][23][24], and Gravitational Clustering [25][26][27][28][29]. The literature review on clustering is given in the in the next section.…”

Section: Introductionmentioning

confidence: 99%

Topology: A Theory of a Pseudometric‐Based Clustering Model and Its Application in Content‐Based Image Retrieval

Osuna-Galán

Pérez-Pimentel

Avilés‐Cruz

et al. 2019

Mathematical Problems in Engineering

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The clustering problem has been extensively studied over the last 50 years; however, it still has the attention of researchers. This paper presents a topological basis of a pseudometric-based clustering model which takes into account the local and global topological properties of the data to be clustered, as per the definition of homogeneity measurement. The proposed approach takes into account the homogeneity effect produced when a new particle is added to a group. The additional element can be accumulated in the group if its local homogeneity is not altered and, therefore, it is not necessary to carry out tests in another group. A new group needs to be generated if the threshold of the local homogeneity of the group exceeds. Theoretical results, their implementation, and their application to the problem of Content Based Image Retrieval (CBIR) are presented. The tests were performed using three image databases widely used in the literature, which are “Vogel and Shiele,” “Oliva and Torralba,” and “L. Fei- Fei, R. Fergus and P. Perona.” The results are presented and compared with the most competitive methods available in the literature.

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DENDIS: A new density-based sampling for clustering algorithm

Cited by 35 publications

References 32 publications

A hierarchical clustering algorithm and an improvement of the single linkage criterion to deal with noise

A hierarchical clustering algorithm and an improvement of the single linkage criterion to deal with noise

On the Enhancement of Classification Algorithms Using Biased Samples

Topology: A Theory of a Pseudometric‐Based Clustering Model and Its Application in Content‐Based Image Retrieval

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