Background: Breast cancer, a type of malignant tumor, affects women more than men. About one third of women with breast cancer die of this disease. Hence, it is imperative to find a tool for the proper identification and early treatment of breast cancer. Unlike the conventional data mining algorithms, fuzzy logic based approaches help in the mining of association rules from quantitative transactions.

Methods: In this study a novel fuzzy methodology IFFP (Improved Fuzzy Frequent Pattern Mining), based on a fuzzy association rule mining for biological knowledge extraction, is introduced to analyze the dataset in order to find the core factors that cause breast cancer. This method consists of two phases. During the first phase, fuzzy frequent itemsets are mined using the proposed algorithm IFFP. Fuzzy association rules are formed during the second phase, indicating whether a person belongs to benign or malignant. This algorithm is applied on WBCD (Wisconsin Breast Cancer Database) to detect the presence of breast cancer.

Results: It is determined that the factor, Mitoses has low range of values on both malignant and benign and hence it does not contribute to the detection of breast cancer. On the other hand, the high range of Bare Nuclei shows more chances for the presence of breast cancer.

Conclusion: Experimental evaluations on real datasets show that our proposed method outperforms recently proposed state-of-the-art algorithms in terms of runtime and memory usage.