K-Mean Clustering Algorithm Approach for Data Mining of Heterogeneous Data

Kalra, Monika; Lal, Niranjan; Qamar, Shamimul

doi:10.1007/978-981-10-3920-1_7

Cited by 22 publications

(11 citation statements)

References 3 publications

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“…The k-means algorithm is commonly used in the partition of N-dimensional population into k series based on a sample [52,53]. Where k-series corresponds to the number of clusters to be calculated, arbitrarily specified by the researcher.…”

Section: Groupings By K-means Partitionmentioning

confidence: 99%

“…Alternatively, centroids can also be specified; 2) if the centroids are not specified, they are obtained randomly for each group; 3) by calculating the Euclidean distance, each object is assigned to its closest centroid; 4) the centroids are updated considering the recently incorporated objects; 5) each observation is reviewed with respect to the other clusters to confirm their membership to the respective group. The assignment and update steps are repeated until convergence or the total number of iterations are reached [53]. This method implies advantages when the author has prior knowledge of the analyzed data.…”

Section: Groupings By K-means Partitionmentioning

confidence: 99%

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Cheminformatics Applied to Analytical Pyrolysis of Lignocellulosic Materials

Reyes-Rivera¹

2022

Recent Perspectives in Pyrolysis Research

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Pyrolysis-Gas Chromatography/Mass Spectrometry has been used to characterize a wide variety of polymers. The main objective is to infer the attributes of materials in relation to their chemical composition. Applications of this technique include the development of new improved materials in the industry. Furthermore, due to the growing interest in biorefinery, it has been used to study plant biomass (lignocellulose) as a renewable energy source. This chapter describes a procedure for characterization and classification of polymeric materials using analytical pyrolysis and cheminformatics. Application of omics tools for spectral deconvolution/alignment and compound identification/annotation on the Py-GC/MS chromatograms is also described. Statistical noise is generated by production of numerous small uninformative compounds during pyrolysis. Such noise is reduced by cheminformatics here detailed and this facilitate the interpretation of results. Furthermore, some inferences made by comparison of the identified compounds to those annotated with a biological role in specialized databases are exemplified. This cheminformatic procedure has allowed to characterize in detail, and classify congruently, different lignocellulosic samples, even using different Py-GC/MS equipment. This method can also be applied to characterize other polymers, as well as to make inferences about their structure, function, resistance and health risk based on their chemical composition.

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Section: Groupings By K-means Partitionmentioning

confidence: 99%

Section: Groupings By K-means Partitionmentioning

confidence: 99%

Cheminformatics Applied to Analytical Pyrolysis of Lignocellulosic Materials

Reyes-Rivera¹

2022

Recent Perspectives in Pyrolysis Research

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“…K-Means clustering is a type of unsupervised learning that divides unlabeled data into non-overlapping groups [20]. The algorithm performs the iterative assignment of each data point to one of the K groups based on the least distance of centroids to their feature space.…”

Section: K-means Clusteringmentioning

confidence: 99%

Automatic Detection System of Olive Trees Using Improved K-Means Algorithm

Waleed

Khan

et al. 2020

Remote Sensing

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Olive cultivation over the past few years has spread across Mediterranean countries with Spain being the world’s largest olive producer among them. Because olives are a major part of the economy for such countries keeping records of their tree count and crop yield is of high significance. Manual counting of trees over such large areas is humanly infeasible. To address this problem, we propose an automatic method for the detection and enumeration of olive trees. The algorithm is a multi-step classification system comprising pre-processing, image segmentation, feature extraction, and classification. RGB satellite images were acquired from the Spanish territory and pre-processed to suppress the additive noise. The region of interest was then segmented from the pre-processed images using K-Means segmentation, through which statistical features were extracted and classified. Promising results were achieved for all classifiers, namely Naive Bayesian, Support Vector Machines (SVMs), Random Forest and Multi-Layer Perceptrons (MLPs), at various division ratios of data samples. In a comparison of all the classification algorithms, Random Forest outperformed the rest by an overall accuracy of 97.5% at the division ratio of 70 to 30 for training to testing.

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“…Varied approaches are available in data science to group the data and build based on closeness among the information, density in the dataset, or new neural network presentation. Any of them are K-Means forms of clustering, intensity clustering and self-organization maps [15].…”

Section: Data Setmentioning

confidence: 99%

Analyzing the College Freshmen Aptitude Results using K-means Algorithm

Padirayon¹

2020

IJATCSE

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Universities today are operating in a very complex and highly competitive environment. Huge data available in the educational field bring out the hidden knowledge which is helpful in decision making.Cagayan State University used Rapidminer as an educational data mining (EDM) tool to extract new knowledge from huge standardized aptitude result of 3,393 data stored in an Excel database. Clustering algorithms such as K-Means where k=3 and K-means where k=4 number were used for evaluating students' records run over the Rapidminer application to enable clustering prototype.The prototype was assessed and used internal validation using the silhouette index to classify which clustering algorithms work best. Finally, the study determined the degree of the students' aptitude that can be used for advising students on the most appropriate course that they can take. The K-means algorithms using parameter k=4 was determined as the best parameter in the clustering.

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K-Mean Clustering Algorithm Approach for Data Mining of Heterogeneous Data

Cited by 22 publications

References 3 publications

Cheminformatics Applied to Analytical Pyrolysis of Lignocellulosic Materials

Cheminformatics Applied to Analytical Pyrolysis of Lignocellulosic Materials

Automatic Detection System of Olive Trees Using Improved K-Means Algorithm

Analyzing the College Freshmen Aptitude Results using K-means Algorithm

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