Automatic cluster labeling through Artificial Neural Networks

Lopes, Lucas Araújo; Machado, Vinícius Ponte; Rabelo, Ricardo A. L.

doi:10.1109/ijcnn.2014.6889949

Cited by 5 publications

(6 citation statements)

References 17 publications

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“…As métricas quantidade de parâmetros (QP), total de erros (TE), média da taxa de acertos (MTA) [35] e desvio padrão (DP) foram usadas para verificação dos resultados. A métrica QP define os parâmetros k, GS e IGS com seus respectivos valores a serem utilizados nas base de dados analisadas neste trabalho.…”

Section: Métricas De Verificação De Resultadosunclassified

Group Labeling Methodology Using Distance-based Data Grouping Algorithms

Filho

Machado

Veras

et al. 2020

RITA

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Clustering algorithms are often used to form groups based on the similarity of their members. In this context, understanding a group is just as important as its composition. Identifying, or labeling groups can assist with their interpretation and, consequently, guide decision-making efforts by taking into account the features from each group. Interpreting groups can be beneficial when it is necessary to know what makes an element a part of a given group, what are the main features of a group, and what are the differences and similarities among them. This work describes a method for finding relevant features and generate labels for the elements of each group, uniquely identifying them. This way, our approach solves the problem of finding relevant definitions that can identify groups. The proposed method transforms the standard output of an unsupervised distance-based clustering algorithm into a Pertinence Degree (GP), where each element of the database receives a GP concerning each formed group. The elements with their GPs are used to formulate ranges of values for their attributes. Such ranges can identify the groups uniquely. The labels produced by this approach averaged 94.83% of correct answers for the analyzed databases, allowing a natural interpretation of the generated definitions.

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Section: Métricas De Verificação De Resultadosunclassified

Group Labeling Methodology Using Distance-based Data Grouping Algorithms

Filho

Machado

Veras

et al. 2020

RITA

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“…The parameters present in the approach were selected after a series of preliminary tests. In these tests, different variations were used in the parameter values until it was estimated with the help of the literature (Lopes, Machado, & Rabelo, 2014;Vajda, Rangoni, & Cecotti, 2015) which parameters were optimal for the data set. The results were as follows: the parameter m was 10 and refers to the number of iterations performed to obtain the means of the anns.…”

Section: Resultsmentioning

confidence: 99%

“…This is crucial to finding an optimal grouping. In addition, this algorithm improves both the speed and the accuracy of k-means (Arthur & Vassilvitskii, 2007;Bahmani, Moseley, Vattani, Kumar, & Vassilvitskii, 2012) One of the biggest problems of cluster analysis is not to find the appropriate or more efficient method of segmentation, but rather to interpret it (Lopes, Machado, & Rabelo, 2014). The correct definition of each cluster is not a trivial task, which makes it necessary to identify each element that composes it, in such a way that a label can be assigned to each group.…”

Section: Clustersmentioning

confidence: 99%

“…For this task, the use of neural networks (anns), has been commonly chosen due to its learning ability, tolerance toward errors, and data organization. anns are known to treat nonlinear and/or dynamic problems; additionally, they have a flexible structure that makes them capable of solving a wide variety of complex problems (Hiziroglu, 2013;Lopes et al, 2014;Samarasinghe, 2016;Ultsch, 2002).…”

Section: Neural Network and Labelingmentioning

confidence: 99%

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Determination of the Representative Socioeconomic Level by BSA in the Mexican Republic

Luquín-García

Ruíz

Rojas

et al. 2018

REVISTA PERSPECT. EMPRESARIAL

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The aim of this article is to determine the socioeconomic level (SEL) with disaggregation of the Basic Statistical Area (BSA) in the Mexican Republic. The methodology used is the one established by the Mexican Association of Market Research Agencies (AMAI) along with the National Institute of Statistics and Geography (INEGI). The Clustering of the BSAs was carried out according to variables contained in the Population and Housing Census of 2010, through Gaussian mixture models, learning neural networks and finally, by defining the labels corresponding to each SEL. We found the existence of a representative SEL for each BSA. In addition, the definition of each socioeconomic level shows good results with an average of 90.86% of correctly labeled elements.

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“…The experimental result on synthetic dataset sampled from 15 uniform density functions signifies the quality of their work. Lopes et al (2014) suggested a cluster labelling mechanism using artificial neural network. They have used both supervised and unsupervised learning along with a discretisation model to label the clusters.…”

Section: Literature Surveymentioning

confidence: 99%

Cluster labelling using chi-square-based keyword ranking and mutual information score: a hybrid approach

Roul

Sahay

2017

IJISDC

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Cluster labelling is a technique which provides useful information about the cluster to the end users. In this paper, we propose a novel approach which is the follow-up of our previous work. Our earlier approach generates clusters of web documents by using a modified apriori approach which is more efficient and faster than the traditional apriori approach. To label the clusters, the propose approach used an effective feature selection technique which selects the top features of a cluster. Rather than labelling the cluster with 'bag of words', a concept driven mechanism has been developed which uses the Wikipedia that takes the top features of a cluster as input to generate the possible candidate labels. Mutual information (MI) score technique has been used for ranking the candidate labels and then the topmost candidates are considered as potential labels of a cluster. Experimental results on two benchmark datasets demonstrate the efficiency of our approach.Keywords: candidate label; chi-square; keyword ranking; mutual information; Wikipedia.Reference to this paper should be made as follows: Roul, R.K. and Sahay, S.K. (2017) 'Cluster labelling using chi-square-based keyword ranking and mutual information score: a hybrid approach', Int.

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Automatic cluster labeling through Artificial Neural Networks

Cited by 5 publications

References 17 publications

Group Labeling Methodology Using Distance-based Data Grouping Algorithms

Group Labeling Methodology Using Distance-based Data Grouping Algorithms

Determination of the Representative Socioeconomic Level by BSA in the Mexican Republic

Cluster labelling using chi-square-based keyword ranking and mutual information score: a hybrid approach

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