Abstract
Problem statement: Association rule mining with fuzzy logic was explored by research for effective datamining and classification. Approach: It was used to find all the rules existing in the transactional database that satisfy some minimum support and minimum confidence constraints. Results: In this study, we propose new rule mining technique using fuzzy logic for mining medical data in order to understand and better serve the needs of Multidimensional Breast cancer Data applications. Conclusion: The main objective of multidimensional Medical data mining is to provide the end user with more useful and interesting patterns. Therefore, the main contribution of this study is the proposed and implementation of fuzzy temporal association rule mining algorithm to classify and detect breast cancer from the dataset.
Highlights
A temporal Association rule is a well established data mining technique used to discover co-occurrences of items mainly in temporal sequence data where the data items in the database are usually recorded as binary data
There are a few works that focus on temporal data mining
Often temporal data mining methods must be capable of analyzing data sets that are prohibitively large for conventional time series modeling techniques to handle efficiently
Summary
A temporal Association rule is a well established data mining technique used to discover co-occurrences of items mainly in temporal sequence data where the data items in the database are usually recorded as binary data (present or not present). New data analysis technique such as data mining can be helpful in analysis large and complex medical sequence data set. Bayesian networks (Khan et al, 2010) are often used for classification problems, in which a learner attempts to construct a classifier from a given set of training examples with class labels. Because the values of p and P can be that instance and assumes that all the attributes are estimated from training examples, naive Bayes is conditionally independent given the class. System architecture: The complete implementation system architecture is given in Fig. 1 which include data collection modules, data analysis modules, data preprocess modules, data classification module and Association rule mining Knowledge discovered data output module with all the internal component of proposed work in details. The details of all the component of the proposed model is given in details The proposed algorithm ANOVA T classification and fuzzy D discretization is given in details
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