A Review on K-Mode Clustering Algorithm

Goyal, Manisha; Aggarwal, Shruti

doi:10.26483/ijarcs.v8i7.4301

Cited by 15 publications

(11 citation statements)

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“…Finally, students' responses were clustered. K-mode clustering was applied (Goyal & Aggarwal, 2017) using the library "k-modes". Clustering is defined as the process of grouping similar data based on features (based on students' responses).…”

Section: Methodsmentioning

confidence: 99%

A Study of Difficulties of Students in Learning Programming

Islam¹,

Sheikh²,

Fatima³

et al. 2019

J.educ.soc.sci

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Programming has been a challenging subject for computer science students. A programmer must have strong analytical and reasoning skills to program effectively. In order to inculcate such skills, instructor must prepare their lectures accordingly and employ several novel techniques to develop analytical and problem solving skills into the students. Despite the strong need, there have not been any studies to identify the major challenges faced by programming students. Such a study can help instructor in development of appropriate materials and planning of lectures accordingly. To fulfill this gap, this work performs a survey of 57 students who just completed their first programming course. Students were asked to fill a questionnaire. The questionnaire was analyzed further to determine what are the most challenging concepts for students to learn and when do the students feel most comfortable learning. The correlations among students response were determined and k-mode clustering was also performed. Based on analysis, several recommendations were drawn. These recommendations can help instructor to develop their learning material. The paper concludes that flipped/ inverted class room model is the most appropriate model for teaching programming course.

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Section: Methodsmentioning

confidence: 99%

A Study of Difficulties of Students in Learning Programming

Islam¹,

Sheikh²,

Fatima³

et al. 2019

J.educ.soc.sci

View full text Add to dashboard Cite

show abstract

“…Presenting thorough use cases and analyses of these techniques is beyond the scope of this paper. Instead, we refer the reader interested in a review of image processing techniques to Buades et al (2005) and Goyal et al (2012), and to Punzo et al (2016) for application to HI spectral cube data.…”

Section: Filtering and Fragment Shadersmentioning

confidence: 99%

Real-time colouring and filtering with graphics shaders

Vohl

Fluke

Barnes

et al. 2017

Monthly Notices of the Royal Astronomical Society

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Despite the popularity of the Graphics Processing Unit (GPU) for general purpose computing, one should not forget about the practicality of the GPU for fast scientific visualisation. As astronomers have increasing access to three dimensional (3D) data from instruments and facilities like integral field units and radio interferometers, visualisation techniques such as volume rendering offer means to quickly explore spectral cubes as a whole. As most 3D visualisation techniques have been developed in fields of research like medical imaging and fluid dynamics, many transfer functions are not optimal for astronomical data. We demonstrate how transfer functions and graphics shaders can be exploited to provide new astronomy-specific explorative colouring methods. We present 12 shaders, including four novel transfer functions specifically designed to produce intuitive and informative 3D visualisations of spectral cube data. We compare their utility to classic colour mapping. The remaining shaders highlight how common computation like filtering, smoothing and line ratio algorithms can be integrated as part of the graphics pipeline. We discuss how this can be achieved by utilising the parallelism of modern GPUs along with a shading language, letting astronomers apply these new techniques at interactive frame rates. All shaders investigated in this work are included in the open source software shwirl (Vohl 2017).

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“…Partitioning clustering can be interpreted by the centroidal Voronoi tessellation method in mathematics ( Du and Wong, 2002 ). It can be further specified to

-means ( Forgy, 1965 , Hartigan and Wong, 1979 , Lloyd, 1982 , MacQueen, 1967 ),

-medians ( Charikar and Guha, 2002 ), and

-modes ( Goyal and Aggarwal, 2017 ), where

-means is the most popular. To implement those, one needs to express observations of the data in a metric space, such that a distance measure can be defined.…”

Section: Introductionmentioning

confidence: 99%

Generalized k-means in GLMs with applications to the outbreak of COVID-19 in the United States

Zhang

Lin

2021

Computational Statistics & Data Analysis

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Generalized -means can be combined with any similarity or dissimilarity measure for clustering. Using the well known likelihood ratio or -statistic as the dissimilarity measure, a generalized -means method is proposed to group generalized linear models (GLMs) for exponential family distributions. Given the number of clusters , the proposed method is established by the uniform most powerful unbiased (UMPU) test statistic for the comparison between GLMs. If is unknown, then the proposed method can be combined with generalized liformation criterion (GIC) to automatically select the best for clustering. Both AIC and BIC are investigated as special cases of GIC. Theoretical and simulation results show that the number of clusters can be correctly identified by BIC but not AIC. The proposed method is applied to the state-level daily COVID-19 data in the United States, and it identifies 6 clusters. A further study shows that the models between clusters are significantly different from each other, which confirms the result with 6 clusters.

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A Review on K-Mode Clustering Algorithm

Cited by 15 publications

References 0 publications

A Study of Difficulties of Students in Learning Programming

A Study of Difficulties of Students in Learning Programming

Real-time colouring and filtering with graphics shaders

Generalized k-means in GLMs with applications to the outbreak of COVID-19 in the United States

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