Clustering Mixed Data by Fast Search and Find of Density Peaks

Liu, Shihua; Zhou, Bingzhong; Huang, Decai; Shen, Liangzhong

doi:10.1155/2017/5060842

Cited by 11 publications

(10 citation statements)

References 6 publications

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“…In this section, we show our experiments on some datasets from UCI machine learning repository [54] to validate our proposed DPC-SDAE algorithm by comparing with OCIL [13], k-prototypes [12], and DPC-M [22]. In addition, the evaluation indexes adopted and the impact of five hyperparameters on clustering quality are also introduced.…”

Section: Resultsmentioning

confidence: 99%

“…In the experiments, the clustering performance of our proposed DPC-SDAE clustering algorithm is compared with k-prototypes [12], OCIL [13], and DPC-M [22] algorithms on six different datasets. Among them, k-prototypes is the most classical clustering algorithm for mixed data, and OCIL is an efficient partition-based clustering algorithm being free of certain parameters, while DPC-M proposed in 2017 is an algorithm based on DPC for clustering mixed data by defining a united distance for categorical and numerical attributes.…”

Section: Clustering Results and Analysismentioning

confidence: 99%

“…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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Section: Resultsmentioning

confidence: 99%

Section: Clustering Results and Analysismentioning

confidence: 99%

Section: Clustering Approaches To Mixed Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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A methodology for automatic parameter-tuning and center selection in density-peak clustering methods

García-García

García-Ródenas

2020

Soft Comput

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The density-peak clustering algorithm, which we refer to as DPC, is a novel and efficient density-based clustering approach. The method has the advantage of allowing non-convex clusters, and clusters of variable size and density, to be grouped together, but it also has some limitations, such as the visual location of centers and the parameter tuning. This paper describes an optimization-based methodology for automatic parameter/center selection applicable both to the DPC and to other algorithms derived from it. The objective function is an internal/external cluster validity index, and the decisions are the parameterization of the algorithm and the choice of centers. The internal validation measures lead to an automatic parameter-tuning process, and the external validation measures lead to the so-called optimal rules, which are a tool to bound the performance of a given algorithm from above on the set of parameterizations. A numerical experiment with real data was performed for the DPC and for the fuzzy weighted k-nearest neighbor (FKNN-DPC) which validates the automatic parameter-tuning methodology and demonstrates its efficiency compared to the state of the art.

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