Mining Fuzzy Relationship Between Malignant Tumors and Industrial Pollution

Malignant tumors are one of the serious diseases that endanger human health. The use of data mining techniques to mine the relationship between malignant tumors and various pathogenic factors has been attracted more and more attention. In practice, the relationship between tumor diseases and pathogenic factors is often fuzzy, and the occurrence of tumor diseases is not only affected by a single factor. However, there is no research to address the above problem. For this reason, the concept of fuzzy co-location patterns is proposed that combines the spatial co-location pattern mining with fuzzy theory, and pollution sources are fuzzified by the clustering method. Then, the method of extracting rules from the decision table is adopted to extract rules, and the corresponding confidence calculation algorithm is designed. As a result, a novel method which can discover the fuzzy relationships between tumor diseases and pathogenic factors is proposed. The effectiveness of the proposed method is verified on the actual case, and the effects of different parameters on the running time of the algorithm are analyzed by conducting experiments on synthetic datasets. At last, the theoretical analysis of the time complexity of the proposed algorithm is presented.