A novel density peaks clustering algorithm for mixed data

Du, Mingjing; Ding, Shifei; Xue, Yu

doi:10.1016/j.patrec.2017.07.001

Cited by 52 publications

(16 citation statements)

References 25 publications

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“…Ding et al proposed an entropy-based density peaks clustering algorithm which provides a new similarity measure for either numeric or categorical attributes which has a uniform criterion [39]. Du et al proposed a novel density peaks clustering algorithm for mixed data (DPC-MD) which uses a new similarity criterion to handle the three types of data: numeric, categorical, or mixed data for improving the original density peaks clustering algorithm [40]. Gu et al proposed a modified k-prototypes algorithm to obtain the capacity of self-adaptive in discrete interval determination, which has overcome the shortcomings from common methods in conditional complementary entropy [41].…”

Section: Clustering For Mixed Datamentioning

confidence: 99%

An Efficient Grid-Based K-Prototypes Algorithm for Sustainable Decision-Making on Spatial Objects

Jang

Kim

et al. 2018

Sustainability

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Data mining plays a critical role in sustainable decision-making. Although the k-prototypes algorithm is one of the best-known algorithms for clustering both numeric and categorical data, clustering a large number of spatial objects with mixed numeric and categorical attributes is still inefficient due to complexity. In this paper, we propose an efficient grid-based k-prototypes algorithm, GK-prototypes, which achieves high performance for clustering spatial objects. The first proposed algorithm utilizes both maximum and minimum distance between cluster centers and a cell, which can reduce unnecessary distance calculation. The second proposed algorithm as an extension of the first proposed algorithm, utilizes spatial dependence; spatial data tends to be similar to objects that are close. Each cell has a bitmap index which stores the categorical values of all objects within the same cell for each attribute. This bitmap index can improve performance if the categorical data is skewed. Experimental results show that the proposed algorithms can achieve better performance than the existing pruning techniques of the k-prototypes algorithm.

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Section: Clustering For Mixed Datamentioning

confidence: 99%

An Efficient Grid-Based K-Prototypes Algorithm for Sustainable Decision-Making on Spatial Objects

Jang

Kim

et al. 2018

Sustainability

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“…To make DPC algorithm be applicable for clustering mixed data, DP-MD-FN [14] combined an entropy-based strategy with DPC and employed fuzzy neighborhood to calculate the local density-based on a uniform similarity measure. In addition, some researchers extended DPC by defining other different types of unified similarity or dissimilarity metrics to handle numerical and categorical attributes simultaneously, such as DPC-MD [21] and DPC-M [22]. However, any unified similarity or dissimilarity metric may not be effective for all mixed datasets, and defining a proper unified similarity or dissimilarity metric for a given dataset is not an easy task.…”

Section: Clustering Approaches To Mixed Datamentioning

confidence: 99%

“…Density peaks clustering (referred to as DPC) [20] is an effective clustering algorithm based on local density and relative distance. In order to make DPC algorithm handle mixed data, some algorithms were proposed based on different types of unified similarity or dissimilarity metrics for numerical and categorical attributes, such as DP-MD-FN [14], DPC-MD [21], and DPC-M [22]. In recent years, deep learning has attracted more attention in various fields and many methods have been investigated by integrating deep neural networks and clustering algorithms on numerical data, such as DEC [23], DBC [24], and DNC [25].…”

Section: Introductionmentioning

confidence: 99%

“…(1) DPC-based clustering algorithms for mixed data, such as DP-MD-FN [14], DPC-MD [21], and DPC-M [22], need to select the number of cluster centers by decision graph manually as DPC. (2) Most of deep learning-based clustering algorithms, such as DEC [23], DBC [24], and DNC [25], are only suitable for clustering numerical data with spherical distribution due to integration of the deep learning and k-means algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Clustering Mixed Data Based on Density Peaks and Stacked Denoising Autoencoders

et al. 2019

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With the universal existence of mixed data with numerical and categorical attributes in real world, a variety of clustering algorithms have been developed to discover the potential information hidden in mixed data. Most existing clustering algorithms often compute the distances or similarities between data objects based on original data, which may cause the instability of clustering results because of noise. In this paper, a clustering framework is proposed to explore the grouping structure of the mixed data. First, the transformed categorical attributes by one-hot encoding technique and normalized numerical attributes are input to a stacked denoising autoencoders to learn the internal feature representations. Secondly, based on these feature representations, all the distances between data objects in feature space can be calculated and the local density and relative distance of each data object can be also computed. Thirdly, the density peaks clustering algorithm is improved and employed to allocate all the data objects into different clusters. Finally, experiments conducted on some UCI datasets have demonstrated that our proposed algorithm for clustering mixed data outperforms three baseline algorithms in terms of the clustering accuracy and the rand index.

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“…These existing indices can only regard the number of clusters as a variable rather than other clustering parameters, such as the density threshold in Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm [21] and the grid size in the CLIQUE algorithm [22]. Recently, a density peak-point-based clustering (DPC) algorithm [23] and its variants [24][25][26] have attracted considerable attention; but the number of peak points therein remains so uncertain that the correctness of clustering results is difficult to guarantee.…”

Section: Introductionmentioning

confidence: 99%

Boundary Matching and Interior Connectivity-Based Cluster Validity Anlysis

Yue

Wang

et al. 2020

Applied Sciences

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The evaluation of clustering results plays an important role in clustering analysis. However, the existing validity indices are limited to a specific clustering algorithm, clustering parameter, and assumption in practice. In this paper, we propose a novel validity index to solve the above problems based on two complementary measures: boundary points matching and interior points connectivity. Firstly, when any clustering algorithm is performed on a dataset, we extract all boundary points for the dataset and its partitioned clusters using a nonparametric metric. The measure of boundary points matching is computed. Secondly, the interior points connectivity of both the dataset and all the partitioned clusters are measured. The proposed validity index can evaluate different clustering results on the dataset obtained from different clustering algorithms, which cannot be evaluated by the existing validity indices at all. Experimental results demonstrate that the proposed validity index can evaluate clustering results obtained by using an arbitrary clustering algorithm and find the optimal clustering parameters.

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A novel density peaks clustering algorithm for mixed data

Cited by 52 publications

References 25 publications

An Efficient Grid-Based K-Prototypes Algorithm for Sustainable Decision-Making on Spatial Objects

An Efficient Grid-Based K-Prototypes Algorithm for Sustainable Decision-Making on Spatial Objects

Clustering Mixed Data Based on Density Peaks and Stacked Denoising Autoencoders

Boundary Matching and Interior Connectivity-Based Cluster Validity Anlysis

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