Learning-Based Dissimilarity for Clustering Categorical Data

Rios, Edgar Jacob Rivera; Medina‐Pérez, Miguel Angel; Lazo-Cortés, Manuel S.; Monroy, Raúl

doi:10.3390/app11083509

Cited by 7 publications

(7 citation statements)

References 17 publications

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“…The linear programming model outperformed the traditional and other enhanced k-modes algorithms on categorical datasets [8]. Learning-Based dissimilarity for categorical data clustering outperforms in terms of several performance indicators [16]. Compared to k-modes, the k-Approximate Modal Haplotype achieves an average performance increase of 0.51 percent in Precision and 0.40 percent in Normalized Discounted Cumulative Gain.…”

Section: Related Workmentioning

confidence: 99%

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Firefly Algorithm with Mini Batch K-Means Entropy Measure for Clustering Heterogeneous Categorical Timber Data

Mahfuz¹,

Yusoff²,

Nordin³

et al. 2022

IJACSA

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Clustering analysis is the process of identifying similar patterns in various types of data. Heterogeneous categorical data consists of data on ordinal, nominal, binary, and Likert scales. The clustering solution for heterogeneous data clustering remains difficult due to partitioning complex and dissimilarity features. It is necessary to find a solution to highquality clustering techniques to efficiently determine the significant features of the data. This paper emphasizes using the firefly algorithm to reduce the distance gap between features and improve clustering performance. To obtain an optimal global solution for clustering, we proposed a hybrid of mini-batch kmeans (MBK) clustering-based entropy distance measures (EM) with a firefly optimization algorithm (FA). This study compares the performance of hybrid K-Means, Agglomerative, DBSCAN, and Affinity clustering models with EM and FA. The evaluation uses a variety of data from the timber perception survey dataset. In terms of performance, the proposed MBK+EM+FA has superior and most effective clustering. It achieves a higher accuracy of 96.3 percent, a 97 percent F-measure, a 98 percent precision, and a 97 percent recall. Other external assessments revealed that the Homogeneity (HOMO) is 79.14 percent, the Fowlkes-Mallows Index (FMI) is 93.07 percent, the Completeness (COMP) is 78.04 percent, and the V-Measure (VM) is 78.58 percent. Both proposed MBK+EM+FA and MBK+EM took about 0.45s and 0.35s to compute, respectively. The excellent quality of the clustering results does not justify such time constraints. Surprisingly, the proposed model reduced the distance measure of all heterogeneous features. The future model could put heterogeneous categorical data from a different domain to the test.

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Section: Related Workmentioning

confidence: 99%

“…In the confusion matrix [16], there were four performance metrics such as true positive (TP), false positive (FP), true negative (TN), and false-negative (FN). TP is the metric that could accurately predict the optimized feature from the features in feature space collection.…”

Section: B Parameter Settingsmentioning

confidence: 99%

Firefly Algorithm with Mini Batch K-Means Entropy Measure for Clustering Heterogeneous Categorical Timber Data

Mahfuz¹,

Yusoff²,

Nordin³

et al. 2022

IJACSA

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“…Our proposed improved K-prototypes with the dissimilarity measures in equation ( 8) is compared with other typical clustering algorithms with diferent dissimilarity measures based on various evaluation criteria of clustering algorithms. Rivera-Ríos [22] summarized a variety of dissimilarity measures. Te dissimilarity measures that can be used to measure the mixed attributes, such as Euclidean distance of K-means++, the dissimilarity measure of K-prototypes, and the dissimilarity measure of equi-biased K-prototypes in [19], are selected to compare with our proposed dissimilarity measure.…”

Section: Performance Evaluation Of the Improved K-prototypesmentioning

confidence: 99%

“…Te performance of the clustering algorithm can be evaluated by external evaluation criteria and internal evaluation criteria [22][23][24]. Te internal evaluation criteria are calculated based on the dissimilarity of objects in clusters, and the dissimilarity measure of several algorithms is different, and accordingly, the internal evaluation results of diferent algorithms will not be on the same scale.…”

Section: Performance Evaluation Of the Improved K-prototypesmentioning

confidence: 99%

Ability Grouping of Elderly Individuals Based on an Improved K‐Prototypes Algorithm

Yan-fang

Chu

2023

Mathematical Problems in Engineering

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Elderly individuals with similar abilities are likely to have similar care needs. Abilities are the basis for analysing the need patterns of elderly individuals. Abilities of elderly individuals are both numerical and categorical; therefore, an improved K-prototypes cluster algorithm for mixed attributes was proposed to group elderly individuals based on abilities. In this algorithm, the dissimilarity measure of categorical attributes is designed to consider not only the difference between the object to be allocated and the prototype of the cluster but also the differ6ence between the object to be allocated and other objects in the cluster and can measure the difference in sequential values of ordinal categorical attributes. Experimental results show that the improved K-prototypes algorithm performs well for clustering datasets containing mixed attributes. Taking the CHARLS dataset as an example, distinct groups of elderly individuals based on the improved K-prototypes algorithm showed significant ability differences.

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“…It is important to note that multivariate situations presenting categorical variables or a mix of categorical and numerical variables have been studied within specific areas, such as the processing of mix-type data and categorical data clustering [ 28 , 29 , 30 ]. However, these tools are applicable to observation points, whereas statistical interaction occurs between variables in any given dataset.…”

Section: Introductionmentioning

confidence: 99%

Measuring Interactions in Categorical Datasets Using Multivariate Symmetrical Uncertainty

Gómez-Guerrero

Ortiz

Sosa-Cabrera

et al. 2021

Entropy

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Interaction between variables is often found in statistical models, and it is usually expressed in the model as an additional term when the variables are numeric. However, when the variables are categorical (also known as nominal or qualitative) or mixed numerical-categorical, defining, detecting, and measuring interactions is not a simple task. In this work, based on an entropy-based correlation measure for n nominal variables (named as Multivariate Symmetrical Uncertainty (MSU)), we propose a formal and broader definition for the interaction of the variables. Two series of experiments are presented. In the first series, we observe that datasets where some record types or combinations of categories are absent, forming patterns of records, which often display interactions among their attributes. In the second series, the interaction/non-interaction behavior of a regression model (entirely built on continuous variables) gets successfully replicated under a discretized version of the dataset. It is shown that there is an interaction-wise correspondence between the continuous and the discretized versions of the dataset. Hence, we demonstrate that the proposed definition of interaction enabled by the MSU is a valuable tool for detecting and measuring interactions within linear and non-linear models.

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Learning-Based Dissimilarity for Clustering Categorical Data

Cited by 7 publications

References 17 publications

Firefly Algorithm with Mini Batch K-Means Entropy Measure for Clustering Heterogeneous Categorical Timber Data

Firefly Algorithm with Mini Batch K-Means Entropy Measure for Clustering Heterogeneous Categorical Timber Data

Ability Grouping of Elderly Individuals Based on an Improved K‐Prototypes Algorithm

Measuring Interactions in Categorical Datasets Using Multivariate Symmetrical Uncertainty

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