Comparison of K-Means clustering and statistical outliers in reducing medical datasets

Santhanam, T.; Padmavathi, M.

doi:10.1109/icsemr.2014.7043602

Cited by 10 publications

(4 citation statements)

References 6 publications

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“…• Reduction: It consists of assuming that data follows a specific model, then estimate and store only the parameters of that model [37,41]. It includes several methods, such as Big Data reduction [42], data noise reduction [43], data compression and decompression [44]. • Transformation: It converts the raw data to an appropriate format for further analysis by going through a series of specific steps [45].…”

Section: Data Pre-processingmentioning

confidence: 99%

Big data monetization throughout Big Data Value Chain: a comprehensive review

et al. 2020

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Value creation is the rational goal of any organization. However, in a changing world, in terms of data, it is very difficult to find strategies to sustainably generate new value and improve operations. In this respect, generating value may mean revisiting some deeplyheld conceptions of different processes. For this, each organization has to ensure an efficient and updated management of value creation processes for both inter-organization and partners. Value chain (VC) is one of the key solutions to such dilemmas.

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Section: Data Pre-processingmentioning

confidence: 99%

Big data monetization throughout Big Data Value Chain: a comprehensive review

et al. 2020

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“…Santhanam, T. and Padmavathi, M., S., 2014, [18] compared K-Means clustering and Statistical Outlier detection techniques on cleaned datasets and they found that the reduction rate of Outliers was less than K-Means clustering.…”

Section: Literature Reviewmentioning

confidence: 99%

Forestalling Growth Rate in Type Ii Diabetic Patients Using Data Mining and Artificial Neural Networks: An Intense Survey

Dubey¹,

Shrivastava²

2019

IJCET

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The race for urbanization and thirst for high living status leads to unhealthy life. As the result a rapid growth in number of diabetic patients in urban areas approaching to its deadline. In this situation it become a prime necessity for physicians and health workers to recognize accurate growth rate in number of diabetic patients. Artificial Neural Network is used as one of the artificial intelligent technique for forestalling growth rate of type II diabetic patients. Diabetes occurred due to increased level of glucose in blood. In this paper, an intense survey is done for the prediction of Type II diabetes using different Data Mining tools and Artificial Neural Network techniques, is presented. This survey is aimed to recognize and propose an effective technique for earlier prediction of the Type II diabetes. The data mining techniques like C4.5 Classifier, Support Vector Machine and K-Nearest Neighbour are compared for this work with Artificial Neural Network. As the results Artificial Neural Network found with a great accuracy of 89%.

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“…Authors in [6] focus on comparing the results of what is termed statistical outliers with K-means for downsizing medical datasets. These researchers note the importance of proper pre-processing to accurate results in data mining.…”

Section: Literature Reviewmentioning

confidence: 99%