New Approach for K-mean and K-medoids Algorithm

Patel, Abhishek; Singh, Purnima

doi:10.7753/ijcatr0201.1001

Cited by 18 publications

(20 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Because children were measured based on performance scores on LITMUS-SRT and NWRT designed to identify SLI without penalizing bilinguals, our premise was that SLI-cases would be similar to each other, and hence group together, while TD-cases would form their own cluster regardless of bilingualism. Different from Hamann and Abed Ibrahim (2017), we chose the PAM (Partitioning Around Medoids) non-hierarchical k-medoid clustering method (Kaufman and Rousseeuw, 1987, 2009) over k-means, because it is a suitable method for small datasets with up to approximately 60 objects, and because it can handle noisy data and outliers (Kaufman and Rousseeuw, 1987, 2009; Kashef and Kamel, 2008; Patel and Singh, 2013; Soni and Patel, 2017). Variables were scaled for normalization purposes in the course of the PAM-analysis.…”

Section: Methodsmentioning

confidence: 99%

“…First, in the so-called “Build-step,” the k-medoid algorithm selects k medoids randomly, with k being the optimal number of clusters. Next, a matrix of dissimilarity is calculated from the raw data and the algorithm assigns every object to either of the k clusters based on their distance to the nearest medoid (Patel and Singh, 2013). The sum of absolute error in the clustering procedure is equal to the sum of the distances between data points and their medoids.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

What Machine Learning Can Tell Us About the Role of Language Dominance in the Diagnostic Accuracy of German LITMUS Non-word and Sentence Repetition Tasks

Ibrahim

Fekete

2019

Front. Psychol.

View full text Add to dashboard Cite

The present study investigates the performance of 21 monolingual and 56 bilingual children aged 5;6–9;0 on German LITMUS-sentence-repetition (SRT; Hamann et al., 2013) and non-word-repetition-tasks (NWRT; Grimm et al., 2014), which were constructed in accordance with the LITMUS-principles (Language Impairment Testing in Multilingual Settings; Armon-Lotem et al., 2015). Both tasks incorporate phonologically and syntactically complex structures shown to be cross-linguistically challenging for children with Specific Language Impairment (SLI) and aim at minimizing bias against bilingual children while still being indicative of the presence of language impairment across language combinations (see Marinis and Armon-Lotem, 2015; for sentence-repetition; Chiat, 2015 for non-word-repetition). Given the great variability in bilingual language exposure and the potential effect of language experience on language performance in bilingual children, we examined whether background variables related to bilingualism, particularly, the degree language dominance as measured by relative amount of use and exposure, could compromise the diagnostic accuracy of the German LITMUS-SRT and NWRT. We further investigated whether a combination of the two tasks provides better diagnostic accuracy and helps avoid cases of misdiagnosis. To address this, we used an unsupervised machine learning algorithm, the Partitioning-Around-Medoids (PAM, Kaufman and Rousseeuw, 2009), for deriving a clinical category for the children as ± language-impaired based on their performance scores on SRT and NWRT (in isolation and combined) while withholding information about their clinical status based on standardized assessment in their first (home language, L1) and second language (societal language, L2). Subsequently, we calculated diagnostic accuracy and used regression analysis to investigate which background variables (age of onset, length of exposure, degree of language dominance, socio-economic-status, and risk factors for SLI) best explained clinical-group-membership yielded from the PAM-analysis based on the children’s NWRT and SRT performance scores. Results show that although language-dominance clearly influences the performance of bilingual typically developing children, especially in the SRT, the diagnostic accuracy of the tools is not compromised by language dominance: while risk factors for SLI were significant predictors for clinical group membership in all models, language dominance did not contribute at all to explaining clinical cluster membership as typically developing or SLI based on any of the combinations of the SRT and NWRT variables. Additionally, results confirm that a combination of SRT scored by correct target structure and the structurally more complex language-dependent part of the NWRT yields better diagnostic accuracy than single measures and is only sensitive to risk factors for SLI and not to dominance levels or SES.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

What Machine Learning Can Tell Us About the Role of Language Dominance in the Diagnostic Accuracy of German LITMUS Non-word and Sentence Repetition Tasks

Ibrahim

Fekete

2019

Front. Psychol.

View full text Add to dashboard Cite

show abstract

“…To solve this problem, the K-means algorithm repeats two procedures by fixing either w ij or b j [8]. The Algorithm 1 consists of the following steps [61]: Make k clusters by assigning data points to the closest centroid Recalculate the centroid of each cluster by the mean of the data in the cluster UNTIL the centroids do not change K-means clustering has been extensively applied to group reservoir models in petroleum engineering [13,16,45,48]. Since reservoir models usually have high dimensionality, some researchers have attempted to perform the algorithm on the featured plane using feature extraction methods, such as PCA and singular value decomposition [5,24,25].…”

Section: K-means Clusteringmentioning

confidence: 99%

“…Even if the K-means algorithm is more popular in petroleum engineering research area due to its calculation efficiency, K-medoids algorithm has actively been employed for grouping models [43,64]. The detailed steps of the Algorithm 2 are listed as follows [61]:…”

Section: K-medoids Clusteringmentioning

confidence: 99%

Efficient Assessment of Reservoir Uncertainty Using Distance-Based Clustering: A Review

et al. 2019

View full text Add to dashboard Cite

This paper presents a review of 71 research papers related to a distance-based clustering (DBC) technique for efficiently assessing reservoir uncertainty. The key to DBC is to select a few models that can represent hundreds of possible reservoir models. DBC is defined as a combination of four technical processes: distance definition, distance matrix construction, dimensional reduction, and clustering. In this paper, we review the algorithms employed in each step. For distance calculation, Minkowski distance is recommended with even order due to sign problem. In the case of clustering, K-means algorithm has been commonly used. DBC has been applied to various reservoir types from channel to unconventional reservoirs. DBC is effective for unconventional resources and enhanced oil recovery projects that have a significant advantage of reducing the number of reservoir simulations. Recently, DBC studies have been performed with deep learning algorithms for feature extraction to define a distance and for effective clustering.

show abstract

“…The k-means method is based on the centroid techniques to represent the cluster and it is sensitive to outliers. This means, a data object with an extremely large value may disrupt the distribution of data [6].…”

Section: K-medoidmentioning

confidence: 99%

Analysis and Implementation of Modified K-Medoids Algorithm to Increase Scalability and Efficiency for Large Dataset

Gandhi¹,

Srivastava²

2014

IJRET

View full text Add to dashboard Cite

Clustering plays a vital role in research area in the field of data mining. Clustering is a process of partitioning a set of data in a meaningful sub classes called clusters. It helps users to understand the natural grouping of cluster from the data set. It is unsupervised classification that means it has no predefined classes. Applications of cluster analysis are Economic Science, Document classification, Pattern Recognition, Image Processing, text mining. Hence, in this study some algorithms are presented which can be used according to one's requirement. K-means is the most popular algorithm used for the purpose of data segmentation. K-means is not very effective in many cases. Also it is not even applicable for data segmentation in some specific kinds of matrices like Absolute Pearson. Whereas K-Medoids is considered flexible than k-means and also carry compatibility to work with almost every type of data matrix. The medoid computed using k-Medoids algorithm is roughly comparable to the median. After checking the literature on median, we have found a number of advantages of median over arithmetic mean. In this paper, we have used a modified version of k-medoids algorithm for the large data sets. Proposed k-medoids algorithm has been modified to perform faster than k-means because speed is the major cause behind the k-medoids unpopularity as compared to kmeans. Our experimental results have shown that improved k-medoid performed better than k-means and k-medoid in terms of cluster quality and elapsed time

show abstract

New Approach for K-mean and K-medoids Algorithm

Cited by 18 publications

References 3 publications

What Machine Learning Can Tell Us About the Role of Language Dominance in the Diagnostic Accuracy of German LITMUS Non-word and Sentence Repetition Tasks

What Machine Learning Can Tell Us About the Role of Language Dominance in the Diagnostic Accuracy of German LITMUS Non-word and Sentence Repetition Tasks

Efficient Assessment of Reservoir Uncertainty Using Distance-Based Clustering: A Review

Analysis and Implementation of Modified K-Medoids Algorithm to Increase Scalability and Efficiency for Large Dataset

Contact Info

Product

Resources

About