RECOME: A new density-based clustering algorithm using relative KNN kernel density

Geng, Yangli-ao; Li, Qingyong; Zheng, Rong; Zhuang, Fuzhen; He, Ruisi; Xiong, Naixue

doi:10.1016/j.ins.2018.01.013

Cited by 55 publications

(19 citation statements)

References 29 publications

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“…Usually, the regions contain points with high density in the data space, which makes density-based clustering mistake them as clusters [59]. Mechanisms of aggregation in density can characterize the clustering [45]. A significant advantage of density-based clustering is that it can discover differently shaped clusters and noise from data [22,24,63].…”

Section: Category 4: Density-based Clusteringmentioning

confidence: 99%

A Review of Computational Methods for Clustering Genes with Similar Biological Functions

et al. 2019

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Clustering techniques can group genes based on similarity in biological functions. However, the drawback of using clustering techniques is the inability to identify an optimal number of potential clusters beforehand. Several existing optimization techniques can address the issue. Besides, clustering validation can predict the possible number of potential clusters and hence increase the chances of identifying biologically informative genes. This paper reviews and provides examples of existing methods for clustering genes, optimization of the objective function, and clustering validation. Clustering techniques can be categorized into partitioning, hierarchical, grid-based, and density-based techniques. We also highlight the advantages and the disadvantages of each category. To optimize the objective function, here we introduce the swarm intelligence technique and compare the performances of other methods. Moreover, we discuss the differences of measurements between internal and external criteria to validate a cluster quality. We also investigate the performance of several clustering techniques by applying them on a leukemia dataset. The results show that grid-based clustering techniques provide better classification accuracy; however, partitioning clustering techniques are superior in identifying prognostic markers of leukemia. Therefore, this review suggests combining clustering techniques such as CLIQUE and k-means to yield high-quality gene clusters.

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Section: Category 4: Density-based Clusteringmentioning

confidence: 99%

A Review of Computational Methods for Clustering Genes with Similar Biological Functions

et al. 2019

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“…Unfortunately, the performance of DBSCAN and most density-based clustering methods depend heavily on parameter tuning, which generally comes down to finding the right amount of smoothing for density estimation. This problem also applies also to k nearest neighbours (kNN)-based methods [29][30][31] as k is generally not an intuitive parameter for end-users. Automatic tuning with the elbow method [32,33] gives no theoretical guarantee of finding the optimal parameter.…”

Section: Related Workmentioning

confidence: 99%

Minimax Bridgeness-Based Clustering for Hyperspectral Data

Moan

Cariou

2020

Remote Sensing

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Hyperspectral (HS) imaging has been used extensively in remote sensing applications like agriculture, forestry, geology and marine science. HS pixel classification is an important task to help identify different classes of materials within a scene, such as different types of crops on a farm. However, this task is significantly hindered by the fact that HS pixels typically form high-dimensional clusters of arbitrary sizes and shapes in the feature space spanned by all spectral channels. This is even more of a challenge when ground truth data is difficult to obtain and when there is no reliable prior information about these clusters (e.g., number, typical shape, intrinsic dimensionality). In this letter, we present a new graph-based clustering approach for hyperspectral data mining that does not require ground truth data nor parameter tuning. It is based on the minimax distance, a measure of similarity between vertices on a graph. Using the silhouette index, we demonstrate that the minimax distance is more suitable to identify clusters in raw hyperspectral data than two other graph-based similarity measures: mutual proximity and shared nearest neighbours. We then introduce the minimax bridgeness-based clustering approach, and we demonstrate that it can discover clusters of interest in hyperspectral data better than comparable approaches.

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“…R e ⟵ FindECR(x, k, cid); (9) if R e ≠ ∅ (10) d c (cid) ⟵ max y∈R e d h (y) + b; (11) ECRs cid { } ⟵ R e ; (12) cid ⟵ cid + 1; (13) end if (14) else (15) label(x) ⟵ NOISE; (16) end if (17) (1) R e ⟵ {};…”

Section: Procedures Of the Rnn-dhr Algorithmunclassified

“…To remedy these limitations in DPC, there are many improved methods that have been proposed [5,[8][9][10][11][12][13][14]. FKNN-DPC [8] defines a uniform local density metric based on the k-nearest neighbors and uses a fuzzy technique to complete the assignment procedure after the cluster centres have been found out manually.…”

Section: Introductionmentioning

confidence: 99%

“…ADPC [5] calculates local density of each object on its k-nearest neighbors by using Gaussian kernel function and applies the divide-and-conquer strategy to find cluster centres and group other objects automatically. RECOME [10] defines a new density measure as the ratio of each object's density to the maximum density of its k-nearest neighbors and also uses the divide-andconquer strategy to partition a data set. Although these algorithms have improved DPC in some aspects, they still suffer from some drawbacks of centroid-based methods.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Local Density Hierarchical Clustering Algorithm Based on Reverse Nearest Neighbors

Liu

Fang

et al. 2019

Mathematical Problems in Engineering

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Clustering is widely used in data analysis, and density-based methods are developed rapidly in the recent 10 years. Although the state-of-art density peak clustering algorithms are efficient and can detect arbitrary shape clusters, they are nonsphere type of centroid-based methods essentially. In this paper, a novel local density hierarchical clustering algorithm based on reverse nearest neighbors, RNN-LDH, is proposed. By constructing and using a reverse nearest neighbor graph, the extended core regions are found out as initial clusters. Then, a new local density metric is defined to calculate the density of each object; meanwhile, the density hierarchical relationships among the objects are built according to their densities and neighbor relations. Finally, each unclustered object is classified to one of the initial clusters or noise. Results of experiments on synthetic and real data sets show that RNN-LDH outperforms the current clustering methods based on density peak or reverse nearest neighbors.

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RECOME: A new density-based clustering algorithm using relative KNN kernel density

Cited by 55 publications

References 29 publications

A Review of Computational Methods for Clustering Genes with Similar Biological Functions

A Review of Computational Methods for Clustering Genes with Similar Biological Functions

Minimax Bridgeness-Based Clustering for Hyperspectral Data

A Novel Local Density Hierarchical Clustering Algorithm Based on Reverse Nearest Neighbors

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