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

Fuzzy Logic with Data Mining with respect to Prediction and Clustering - Research Paper Example According to Jemal and Ferlay (2004, p.69), breast cancer is currently one of the major health problems as well as the leading cause of death amongst women worldwide. Consequently early detection of cancer risks is one of the key ways of improving the prognosis of the disease. Although there are a number radiological techniques such as mammography that can be used in the early detection of breast cancer risks, the enormous data generated by these techniques often make it difficult for radiologists to accurately evaluate breast cancer data (Dorf and Robert, 2001, p.234). Artificial intelligence techniques such as fuzzy clustering algorithms can therefore significantly improve the diagnosis and evaluation of breast cancer risks through clustering of the particular data elements. Consequently the incorporation of fuzzy logic algorithms in data mining is a powerful 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, indistinct borders and different densities. In this regard, a fuzzy clustering algorithm can be one of the most 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 but can also be used to develop accurate results that are easy to implement. One of the greatest potential advantages of incorporating fuzzy logic in data mining is the fact that such algorithms can significantly be used in the modeling of inaccurate, non linear and complex data systems b y implementing human knowledge 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 paper 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 suspected cancer cases. Proposed single If-then fuzzy rule Assuming that we have a classification problem with an n-dimensional c-class pattern 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. Based on the proposed single fuzzy If-then rule that is based on the mean and standard deviation of the attribute values, the fuzzy rule will be generated for each of the classes. Consequently the fuzzy If then rule for the kth