Abstract
Application of Data Mining tasks over health care has gained much importance nowadays. Most of the Association Rule Mining techniques attempts to extract only the positive recurrent itemsets and pay less attention towards the negative items. The paper is all about medical assistance, which concentrates on retrieving both positive and negative recurrent itemsets in a efficient way by compressing the overall data available. Stemming methods help in this compression of data to half of its size in order to reduce and save memory space. To analyze data, the clustering technique is applied, especially the k-means clustering is used, as it is found to be more effective, easy and less time consuming method when compared to other clustering flavours.
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Asha, P., Albert Mayan, J., Canessane, A. (2018). Efficient Mining of Positive and Negative Itemsets Using K-Means Clustering to Access the Risk of Cancer Patients. In: Zelinka, I., Senkerik, R., Panda, G., Lekshmi Kanthan, P. (eds) Soft Computing Systems. ICSCS 2018. Communications in Computer and Information Science, vol 837. Springer, Singapore. https://doi.org/10.1007/978-981-13-1936-5_40
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DOI: https://doi.org/10.1007/978-981-13-1936-5_40
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