The quick dynamic clustering method for mixed-type data

Ayuyev, Vadim V.; Thura, A.; Hlaing, Nwe Nwe; Loginova, M. B.

doi:10.1134/s0005117912120120

Cited by 2 publications

(1 citation statement)

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“…Impact of dynamic clusters: Segmentation of consumers is becoming increasingly challenging these days with mixed attributes and missing data. With the same product belonging to multiple clusters and certain data not being relevant for clustering, the typical fixed-length clustering cannot be employed (Ayuyev et. al, 2012).…”

Section: Impact Of 3-d Printingmentioning

confidence: 99%

A systematic review of Sustainability of Digital Retail System

2020

rmc

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The purpose of this paper is to understand the sustainability of the digital retail system. As the retail world has embraced digital to track consumers' needs right from their tweets to online retail, actual achievement for th e seller is to motivate consumers to want what is the best for them, the seller, the society and the planet. In this study the authors have used systematic literature review (SLR) approach to identify the factors or variables from extant literature. The effect of these variables on the consumer behaviour was then analysed through exploratory factor analysis of the results of the responses to a structured questionnaire. The study finds out that the not just mere online shopping would be the retail of the fut ure, but a mix of both the physical and online experience, as the SLR output suggests. Shoppers at home should be able to visit virtual outlets and in-store shoppers should be able to access a cloud of information about products, so that the consumer experience will be more tailored, more continuous and more omnipresent. Like any study, this study has its own limitations. In this stud y, the authors have developed a model based on expert opinion. Though the sample size may not be enough to validate the model statistically, nevertheless, it can be regarded as a platform for further research study.

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Section: Impact Of 3-d Printingmentioning

confidence: 99%

A systematic review of Sustainability of Digital Retail System

2020

rmc

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A composite method to infer drug resistance with mixed genomic data

Strong²,

Sm³

2020

Preprint

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Background: The increasing incidence of drug resistance in tuberculosis and other infectious diseases poses an escalating cause for concern, emphasizing the urgent need to devise robust computational and molecular methods identify drug resistant strains. Although machine learning-based approaches using whole-genome sequence data can facilitate the inference of drug resistance, current implementations do not optimally take advantage of information in public databases and are not robust for small sample sizes and mixed attribute types. Results: In this paper we introduce the Composite MetaDistance method, an approach for feature selection and classification of high-dimensional, unbalanced datasets with mixed attribute features from various data sources. We introduce a mixed-attribute, multi-view distance function to calculate distances between samples, with optimal handling of nominal features and different feature views. We also introduce a novel feature set for drug resistance prediction in Mycobacterium tuberculosis, using data from diverse sources. We compare the performance of Composite MetaDistance to multiple machine learning algorithms for Mycobacterium tuberculosis drug resistance prediction for three drugs. Composite MetaDistance consistently outperforms existing algorithms for small sample training sets, and performs as well as other algorithms for training sets with larger sample sizes. Conclusion: The feature set formulation introduced in this paper is utilizes mutational and publicly available information for each gene, and is much richer than ever devised previously. The prediction algorithm, Composite MetaDistance, is sample size agnostic and robust especially given small sample sizes. Proper handling of nominal features improves performance even with a very small number of nominal features. We expect Composite MetaDistance to be even more robust for datasets with a higher percentage of nominal features. The algorithm is application independent and can be used for any mixed attribute dataset.

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The quick dynamic clustering method for mixed-type data

Cited by 2 publications

References 5 publications

A systematic review of Sustainability of Digital Retail System

A systematic review of Sustainability of Digital Retail System

A composite method to infer drug resistance with mixed genomic data

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