An Interval Type-2 Fuzzy Association Rule Mining Approach to Pattern Discovery in Breast Cancer Dataset

In the literature, several methods explored to analyze breast cancer dataset have failed to sufficiently handle quantitative attribute sharp boundary problem to resolve inter and intra uncertainties in breast cancer dataset analysis. In this study an Interval Type-2 fuzzy association rule mining approach is proposed for pattern discovery in breast cancer dataset. In the first part of this analysis, the interval Type-2 fuzzification of the breast cancer dataset is carried out using Hao and Mendel approach. In the second part, FP-growth algorithm is adopted for associative pattern discovery from the fuzzified dataset from the first part. To define the intuitive words for breast cancer determinant factors and expert data interval, thirty (30) medical experts from specialized hospitals were consulted through questionnaire poling method. To establish the adequacy of the linguistic word defined by the expert, Jaccard similarity measure is used. This analysis is able to discover associative rules with minimum number of symptoms at confidence values as high as 91%. It also identifies High Bare Nuclei and High Uniformity of Cell Shape as strong determinant factors for diagnosing breast cancer. The proposed approach performed better in terms of rules generated when compared with traditional quantitative association rule mining. It is able to eliminate redundant rules which reduce the number of generated rules by 39.5% and memory usage by 22.6%. The discovered rules are viable in building a comprehensive and compact expert driven knowledge-base for breast cancer decision support or expert system.