A Soft Davies-Bouldin Separation Measure

Vergani, Alberto Arturo; Binaghi, Elisabetta

doi:10.1109/fuzz-ieee.2018.8491581

Cited by 21 publications

(11 citation statements)

References 26 publications

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“…In this study, we defined three types of metrics to assess the clustering performance. (1) Silhouette Coefficient (SC) (Dinh et al, 2019); (2) Calinski-Harabasz Index (CHI) (Łukasik et al, 2016); (3) Davies-Bouldin Index (DBI) (Vergani and Binaghi, 2018).…”

Section: Clustering Performance Evaluationmentioning

confidence: 99%

A Novel Computational Framework for Precision Diagnosis and Subtype Discovery of Plant With Lesion

et al. 2022

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Plants are often attacked by various pathogens during their growth, which may cause environmental pollution, food shortages, or economic losses in a certain area. Integration of high throughput phenomics data and computer vision (CV) provides a great opportunity to realize plant disease diagnosis in the early stage and uncover the subtype or stage patterns in the disease progression. In this study, we proposed a novel computational framework for plant disease identification and subtype discovery through a deep-embedding image-clustering strategy, Weighted Distance Metric and the t-stochastic neighbor embedding algorithm (WDM-tSNE). To verify the effectiveness, we applied our method on four public datasets of images. The results demonstrated that the newly developed tool is capable of identifying the plant disease and further uncover the underlying subtypes associated with pathogenic resistance. In summary, the current framework provides great clustering performance for the root or leave images of diseased plants with pronounced disease spots or symptoms.

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Section: Clustering Performance Evaluationmentioning

confidence: 99%

A Novel Computational Framework for Precision Diagnosis and Subtype Discovery of Plant With Lesion

et al. 2022

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“…Whereas, internal validation provides a good score to the algorithms that produce high similarity within a cluster and low similarity between clusters. Davies Bouldin Index [36], Dunn Index [37] and Silhouette Index [38] are the popular methods for internal validation measure. There are also some new clustering validation indices proposed such as clustering validation index based on nearest neighbors (CVVN index) [39], Local Cores-based Cluster Validity (LCCV index) [40] and Absolute Cluster Validity index [41].…”

Section: Plos Onementioning

confidence: 99%

A clustering approach to identify multidimensional poverty indicators for the bottom 40 percent group

et al. 2021

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The Multidimensional Poverty Index (MPI) is an income-based poverty index which measures multiple deprivations alongside other relevant factors to determine and classify poverty. The implementation of a reliable MPI is one of the significant efforts by the Malaysian government to improve measures in alleviating poverty, in line with the recent policy for Bottom 40 Percent (B40) group. However, using this measurement, only 0.86% of Malaysians are regarded as multidimensionally poor, and this measurement was claimed to be irrelevant for Malaysia as a country that has rapid economic development. Therefore, this study proposes a B40 clustering-based K-Means with cosine similarity architecture to identify the right indicators and dimensions that will provide data driven MPI measurement. In order to evaluate the approach, this study conducted extensive experiments on the Malaysian Census dataset. A series of data preprocessing steps were implemented, including data integration, attribute generation, data filtering, data cleaning, data transformation and attribute selection. The clustering model produced eight clusters of B40 group. The study included a comprehensive clustering analysis to meaningfully understand each of the clusters. The analysis discovered seven indicators of multidimensional poverty from three dimensions encompassing education, living standard and employment. Out of the seven indicators, this study proposed six indicators to be added to the current MPI to establish a more meaningful scenario of the current poverty trend in Malaysia. The outcomes from this study may help the government in properly identifying the B40 group who suffers from financial burden, which could have been currently misclassified.

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“…A combination of VAT and topic models are used in our proposed framework (hence, they called as VNMF, VLDA, VLSI, and VPLSI) and experimented with Euclidean, cosine based and multiviewpoint cosine based metrics. Evaluation of proposed techniques are measured with five external validity indexes, namely, clustering accuracy (CA) [41], normalized mutual information (NMI) [42], precision (P), recall (R) and F-Score (F) [43], [44] and seven internal validity indexes viz., Davies-Bouldin index (DB) [55], [56], [60], Calinski-Harabasz Index (CHI) [55], [56], Silhouette Index (SI) [55], [56], Xie-Beni Index (XI) [57], Partition Coefficient (PC) [59], Partition Entropy Index (PEI) [57], [58], and Separation Measure (SM) [60]. Health tweets are assigned to topic clusters which are maintained the highest similarity with the topic clusters to improve the value of CA.…”

Section: Performance Measures Evaluationmentioning

confidence: 99%

Hybrid Topic Cluster Models for Social Healthcare Data

Prasad¹,

Mohammed²,

Noorullah³

2019

IJACSA

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Social media and in particular, microblogs are becoming an important data source for disease surveillance, behavioral medicine, and public healthcare. Topic Models are widely used in microblog analytics for analyzing and integrating the textual data within a corpus. This paper uses health tweets as microblogs and attempts the health data clustering by topic models. The traditional topic models, such as Latent Semantic Indexing (LSI), Probabilistic Latent Schematic Indexing (PLSI), Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF), and integer Joint NMF(intJNMF) methods are used for health data clustering; however, they are intractable to assess the number of health topic clusters. Proper visualizations are essential to extract the information from and identifying trends of data, as they may include thousands of documents and millions of words. For visualization of topic clouds and health tendency in the document collection, we present hybrid topic models by integrating traditional topic models with VAT. Proposed hybrid topic models viz., Visual Non-negative Matrix Factorization (VNMF), Visual Latent Dirichlet Allocation (VLDA), Visual Probabilistic Latent Schematic Indexing (VPLSI) and Visual Latent Schematic Indexing (VLSI) are promising methods for accessing the health tendency and visualization of topic clusters from benchmarked and Twitter datasets. Evaluation and comparison of hybrid topic models are presented in the experimental section for demonstrating the efficiency with different distance measures, include, Euclidean distance, cosine distance, and multi-viewpoint cosine similarity.

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A Soft Davies-Bouldin Separation Measure

Cited by 21 publications

References 26 publications

A Novel Computational Framework for Precision Diagnosis and Subtype Discovery of Plant With Lesion

A Novel Computational Framework for Precision Diagnosis and Subtype Discovery of Plant With Lesion

A clustering approach to identify multidimensional poverty indicators for the bottom 40 percent group

Hybrid Topic Cluster Models for Social Healthcare Data

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