A Multi-View Clustering Algorithm for Mixed Numeric and Categorical Data

Ji, Jinchao; Li, Ruonan; Pang, Wei; He, Fei; Feng, Guozhong; Zhao, Xiaowei

doi:10.1109/access.2021.3057113

Cited by 9 publications

(7 citation statements)

References 34 publications

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“…Table 10 shows the comparison of evaluation metrics for different datasets. The metrics RI, Precision, and Recall is compared with ABC-K-Prototypes [24], CCS-K-Prototypes [1], and Multi-view K-Prototype [25]. Table 11 and Figure 1 shows the Rand Index comparison.…”

Section: Resultsmentioning

confidence: 99%

Reclust: an efficient clustering algorithm for mixed data based on reclustering and cluster validation

Arockiam¹,

Irudhayaraj

2022

IJEECS

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<span>Clustering is a significant approach in data mining, which seeks to find groups or clusters of data. Both numeric and categorical features are frequently used to define the data in real-world applications. Several different clustering algorithms are proposed for the numerical and categorical datasets. In clustering algorithms, the quality of clustering results is evaluated using cluster validation. This paper proposes an efficient clustering algorithm for mixed numerical and categorical data using re-clustering and cluster validation. Initially, the mixed dataset is clustered with four traditional clustering algorithms like expectation-maximization (EM), hierarchical cluster (HC), k-means (KM), and self-organizing map (SOM). These four algorithms are validated, and the best algorithm is selected for re-clustering. It is an iterative process for improving the quality of cluster results. The incorrectly clustered data is iteratively re-clustered and evaluated based on the cluster validation. The performance of the proposed clustering method is evaluated with a real-time dataset in terms of purity, normalized mutual information, rand index, precision, and recall. The experimental results have shown that the proposed reclust algorithm achieves better performance compared to other clustering algorithms.</span>

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Section: Resultsmentioning

confidence: 99%

Reclust: an efficient clustering algorithm for mixed data based on reclustering and cluster validation

Arockiam¹,

Irudhayaraj

2022

IJEECS

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“…Figure 5 illustrates the comparison between the adjusted Rand index of the new method and others [7,8,12,[24][25][26]. Although it is unfair to compare with other methods because the distances used may vary, from the graph of the ARI the proposed method outperforms others.…”

Section: Evaluation Of Final Groups For Real Datasetsmentioning

confidence: 99%

“…Table 12 depicts the detail of adjusted Rand index values and clustering accuracy from the proposed methods and several others [7,8,12,25,26]. The SFKM method was applied for ionosphere and heart disease case 1, with the same data used to obtain ARI and clustering accuracy.…”

Section: Evaluation Of Final Groups For Real Datasetsmentioning

confidence: 99%

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The Initialization of Flexible K-Medoids Partitioning Method Using a Combination of Deviation and Sum of Variable Values

Kariyam¹,

Abdurakhman²,

Subanar³

et al. 2022

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“…For classification of electricity customers, a twostage clustering methodology is developed to dig valuable information [18]. In [19], a multi-view k-prototypes method is proposed for clustering mixed data using multi-view version, which proves to have better generalization ability in many problems. To deal with the unexplored data, the fuzzy c-means clustering algorithm is developed [20].…”

Section: Introductionmentioning

confidence: 99%

Double-Layer K-Means++ Clustering Method for Evaluation of Dispatchable Potential of Massive Regional 5G Base Stations

et al. 2022

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A Multi-View Clustering Algorithm for Mixed Numeric and Categorical Data

Cited by 9 publications

References 34 publications

Reclust: an efficient clustering algorithm for mixed data based on reclustering and cluster validation

Reclust: an efficient clustering algorithm for mixed data based on reclustering and cluster validation

The Initialization of Flexible K-Medoids Partitioning Method Using a Combination of Deviation and Sum of Variable Values

Double-Layer K-Means++ Clustering Method for Evaluation of Dispatchable Potential of Massive Regional 5G Base Stations

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