Tight clustering for large datasets with an application to gene expression data

Karmakar, Bikram; Das, Santanu; Bhattacharya, Surajit; Sarkar, Rohan; Mukhopadhyay, Indranil

doi:10.1038/s41598-019-39459-w

Cited by 13 publications

(9 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A review of different techniques designed for analysis of microarray data was presented in [18]. Tight clustering algorithm was employed in [19] to minimize time complexity of large microarray gene expression data. An evolutionary uncertain data-clustering algorithm was designed in [20] to determine the similarities among sets of gene expression clusters.…”

Section: Literature Surveymentioning

confidence: 99%

Tanimoto Coefficient Similarity based Mean Shift Gentle Adaptive Boosted Clustering for Genomic Predictive Pattern Analytics

Eastaff*,

Saravanan

2019

IJITEE

View full text Add to dashboard Cite

Gene expression data clustering is a significant problem to be resolved as it provides functional relationships of genes in a biological process. Finding co-expressed groups of genes is a challenging problem. To identify interesting patterns from the given gene expression data set, a Tanimoto Coefficient Similarity based Mean Shift Gentle Adaptive Boosted Clustering (TCS-MSGABC) Model is proposed. TCS-MSGABC model comprises two processes namely feature selection and clustering. In first process, Tanimoto Coefficient Similarity Measurement based Feature selection (TCSM-FS) is introduced to identify relevant gene features based on the similarity value for performing the genomic expression clustering. Tanimoto Coefficient Similarity Value ranges from ‘ ’ to ‘ ’ where ‘ ’ is highest similarity. The gene feature with higher similarity value is taken to perform clustering process. After feature selection, Mean Shift Gentle Adaptive Boosted Clustering (MSGABC) algorithm is carried out in TCS-MSGABC model to cluster the similar gene expression data based on the selected features. The MSGABC algorithm is a boosting method for combining the many weak clustering results into one strong learner. By this way, the similar gene expression data are clustered with higher accuracy with minimal time. Experimental evaluation of TCS-MSGABC model is carried out on factors such as clustering accuracy, clustering time and error rate with respect to number of gene data. The experimental results show that the TCS-MSGABC model is able to increases the clustering accuracy and also minimizes clustering time of genomic predictive pattern analytics as compared to state-of-the-art works.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Tanimoto Coefficient Similarity based Mean Shift Gentle Adaptive Boosted Clustering for Genomic Predictive Pattern Analytics

Eastaff*,

Saravanan

2019

IJITEE

View full text Add to dashboard Cite

show abstract

“…The Python3 geospatial data abstraction library may be used for converting satellite image *.tiff files to *.csv files, and R (computer language) is used for unsupervised learning algorithms including those used by AI currently. Increasingly the R libraries can optimize numbers of clusters for pairwise plotting of feature bands and cluster validation (Karmakar et al., 2019; R Core Team, 2022). However, where vast distances need classification for soil physical and chemical properties via index correction or vector algorithms, these big‐data clusters create significant processing bottlenecks (compared with genetic data clustering, which also use this type of approach).…”

Section: Introductionmentioning

confidence: 99%

Heuristics‐enhanced geospatial machine learning (SaaS) of an ancient Mediterranean environment

Svatos

Ahmed

Recaldes

et al. 2022

Soil Science Soc of Amer J

View full text Add to dashboard Cite

Raw soil core physical data used in machine learning algorithms with corresponding spatial remotely sensed data is an emerging science. Using data derived from soil core samples previously collected in Universal Transverse Mercator zone 50 (Western Australia) and remotely sensed data, a model that predicted ground movement (GM) was developed specific to Australian Standards manual AS 1726-2017. This is the first approach for Australian soils and first in the world for soils older than 200 million yr. The model developed reliably predicted GM with 91.1% accuracy. The error obtained from the prediction is within acceptable limits currently used by engineers in calculations concerning soil classification for engineering purposes. Concerning the remotely sensed data analyzed, accuracy of the Atterberg limits method might be improved if additional information about soil structure (layering and horizon) or other variables (seasonal data) are built into this model. This model can be used to save on construction material costs, reduce the potential for human error associated with data collection and sample manipulation, but also fast-track (by up to 6 wk based on current wait times) building approvals while ensuring compliance to the relevant legislation. This platform also reduces the environmental effects of invasive drilling techniques. A requirement within principles of sustainable building practices, and associated with current standards commonly used by structural engineers who may seek better understanding of soil properties in Australia as a software service (with application potential in North America).

show abstract

“…Alternative partial classification rules include the tight clustering algorithm introduced in Tseng and Wong (2005) and extended to deal with large datasets in Karmakar, Das, Bhattacharya, Sarkar, and Mukhopadhyay (2019). In the latter work, when applying the extended tight clustering algorithm to a dataset consisting of more than 50,000 gene expression probes for individuals suffering from psoriasis, more than 30,000 probes were not classified in any of the six clusters that were identified.…”

Section: Introductionmentioning

confidence: 99%

Error rate control for classification rules in multiclass mixture models

Mary‐Huard

Perduca

Martin-Magniette

et al. 2021

The International Journal of Biostatistics

View full text Add to dashboard Cite

In the context of finite mixture models one considers the problem of classifying as many observations as possible in the classes of interest while controlling the classification error rate in these same classes. Similar to what is done in the framework of statistical test theory, different type I and type II-like classification error rates can be defined, along with their associated optimal rules, where optimality is defined as minimizing type II error rate while controlling type I error rate at some nominal level. It is first shown that finding an optimal classification rule boils down to searching an optimal region in the observation space where to apply the classical Maximum A Posteriori (MAP) rule. Depending on the misclassification rate to be controlled, the shape of the optimal region is provided, along with a heuristic to compute the optimal classification rule in practice. In particular, a multiclass FDR-like optimal rule is defined and compared to the thresholded MAP rules that is used in most applications. It is shown on both simulated and real datasets that the FDR-like optimal rule may be significantly less conservative than the thresholded MAP rule.

show abstract

Tight clustering for large datasets with an application to gene expression data

Cited by 13 publications

References 31 publications

Tanimoto Coefficient Similarity based Mean Shift Gentle Adaptive Boosted Clustering for Genomic Predictive Pattern Analytics

Tanimoto Coefficient Similarity based Mean Shift Gentle Adaptive Boosted Clustering for Genomic Predictive Pattern Analytics

Heuristics‐enhanced geospatial machine learning (SaaS) of an ancient Mediterranean environment

Error rate control for classification rules in multiclass mixture models

Contact Info

Product

Resources

About