Fuzzy Logic with Data Mining with respect to Prediction and Clustering Research Paper

groggy Logic with Data Mining with respect to prescience and Clustering - Research Paper ExampleAccording to Jemal and Ferlay (2004, p.69), dresser cancer is currently one of the major health problems as well as the leading cause of death amongst women worldwide. Consequently early catching of cancer risks is one of the key ways of improving the prognosis of the disease. Although there be a moment radiological techniques such as mammography that can be used in the early detection of chest of drawers cancer risks, the enormous information generated by these techniques often make it difficult for radiologists to accurately evaluate thorax cancer data (Dorf and Robert, 2001, p.234). Artificial intelligence techniques such as hairy forgathering algorithms can thusly significantly improve the diagnosis and evaluation of breast cancer risks through thud of the particular data elements. Consequently the incorporation of fuzzy logic algorithms in data minelaying is a powerfu l tool that can be employed in the extraction, clustering, quantification and analysis of the data base information regarding the assessment and diagnosis of cancer risks. When dealing with uncertainties in databases, fuzzy logic clustering algorithms can be used to cluster different elements of data into various membership levels depending on their closeness (Castillo and Melin, 2008, p.94). For example, during the evaluation of breast cancer risks, mammogram data may possess some degree of fuzziness such as ill defined shapes, bedimmed borders and different densities. In this regard, a fuzzy clustering algorithm can be one of the roughly effective ways of handling the fuzziness of data related to breast cancer. As an intelligent technique, Fuzzy logic data mining algorithms not only provide excellent analysis of the data unless can also be used to develop accurate results that are easy to implement. One of the sterling(prenominal) potential advantages of incorporating fuzzy l ogic in data mining is the fact that such algorithms can significantly be used in the modeling of inaccurate, non linear and complex data systems by implementing human noesis and experience as a set of fuzzy rules that uses fuzzy variables for inference purposes (Nguyen and Walker, 2003, p. 96). For example when using fuzzy algorithm for the prediction and clustering of breast cancer data, the human experience and knowledge related to breast cancer risks can be expressed as a set of inference rules of deduction that are then attached to the fuzzy logic system. Another important advantage of fuzzy algorithms systems for prediction and clustering of breast cancer data is that they usually have a significantly high inference speed. This newspaper publisher proposes a fuzzy clustering algorithm that can be used in the data mining of breast cancer data and consequently in the evaluation and prediction of cancer risks in patients with suspect cancer cases. Proposed single If-then fuzzy rule Assuming that we have a classification problem with an n-dimensional c-class digit whose space is given by n-dimensional cube (0, 1), n as well as that the m patterns Xp=Xp1,Xpn, where p=1,2,..m, we will need to generate the fuzzy if then rule in which Xpi 0,1 for p=1,2,., m, i =1,2,..,n. base on the proposed single fuzzy If-then rule that is based on the mean and standard deviation of the set apart values, the fuzzy rule will be generated for each of the classes. Consequently the fuzzy If then rule for the kth