Enhancing Feature Selection with Density Cluster for Better Clustering

Chen, Yang; Li, Hui; Chen, Mei; Dai, Zhenyu; Li, Huanjun; Zhu, Ming

doi:10.1007/978-3-030-00211-4_15

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…As reported in Table 8, the clustering technique of a large scale dataset provides a better result for the proposed method than the existing methods. The epileptic seizure [16] and EEG eye state [45] datasets are considered here as large datasets which provides an accuracy rate of about 0.828 and 0.9506. Similarly, the NMI rate of epileptic seizure and EEG eye state are 0.9739 and 0.9777.…”

Section: Performance and Comparative Analysis Of Large Dataset (Clustmentioning

confidence: 99%

An Efficient Technique for Disease Prediction by Using Enhanced Machine Learning Algorithms for Categorical Medical Dataset

Anusuya

Gomathi²

2021

ITC

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In the 20th century, it is evident that there is a massive evolution of chronic diseases. The data mining approaches beneficial in making some medicinal decisions for curing diseases. But medical data may consist of a large number of data, which makes the prediction process a very difficult one. Also, in the medical field, the dataset may involve both the small database and extensive database. This creates the study of a complex one for disease prediction mechanism. Hence, in this paper, we intend to use a practical machine learning approach for disease prediction of both large and small datasets. Among the various machine learning procedures, classification, and clusters method play a significant role. Therefore, we introduced the enhanced classification and clusters approach in this work for obtaining better accuracy results for disease prediction. In this proposed method, a process of preprocessing is involved, followed by Eigen vector extraction, feature selection, and classification Further, the most suitable features are selected with the use of Multi-Objective based Ant Colony Optimization (MO-ACO) from the extracted features for increasing the classification and clusters. Here we have shown the novelty in every stage of the implementation, such as feature selection, feature extraction, and the final prediction stage. The proposed method will be compared with the existing technique on the measure of precision, NMI, execution time, recall, and Accuracy. Here we conclude with the solution having more accuracy for both small and large datasets.

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Section: Performance and Comparative Analysis Of Large Dataset (Clustmentioning

confidence: 99%