Data mining refers to extracting knowledge from large amount of data. Real life data mining appro... more Data mining refers to extracting knowledge from large amount of data. Real life data mining approaches are interesting because they often present a different set of problems for data miners. The process of designing a model helps to identify the different Cataract Diseases. A cataract can cause a decrease in visual function, which in turn can be classified as a visual disability. Thus, cataract can be defined in three ways. The first definition is an objective lens change. The second is a lens opacity that is associated with a defined level of visual acuity loss. The third relates to the functional consequences of lens opacification. This guideline focuses on the last definition. It deals with care of the patient with functional impairment due to cataract and improvement in function as a result of treatment for the condition. Taking into account the prevalence of cataract among men and women the study is aimed at finding out the characteristics that determine the presence of catarac...
IEEE Transactions on Evolutionary Computation, 2008
Data mining is most commonly used in attempts to induce association rules from transaction data. ... more Data mining is most commonly used in attempts to induce association rules from transaction data. Most previous studies focused on binary-valued transaction data. Transaction data in real-world applications, however, usually consist of quantitative values. This paper, thus, proposes a fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions. A genetic algorithm (GA)-based framework for finding membership functions suitable for mining problems is proposed. The fitness of each set of membership functions is evaluated by the fuzzy-supports of the linguistic terms in the large 1-itemsets and by the suitability of the derived membership functions. The evaluation by the fuzzy supports of large 1-itemsets is much faster than that when considering all itemsets or interesting association rules. It can also help divide-and-conquer the derivation process of the membership functions for different items. The proposed GA framework, thus, maintains multiple populations, each for one item's membership functions. The final best sets of membership functions in all the populations are then gathered together to be used for mining fuzzy association rules. Experiments are conducted to analyze different fitness functions and set different fitness functions and setting different supports and confidences. Experiments are also conducted to compare the proposed algorithm, the one with uniform fuzzy partition, and the existing one without divide-and-conquer, with results validating the performance of the proposed algorithm.
International Journal of Computer Applications Technology and Research, 2014
Data mining refers to extracting knowledge from large amount of data. Real life data mining appro... more Data mining refers to extracting knowledge from large amount of data. Real life data mining approaches are interesting because they often present a different set of problems for diabetic patient's data. The research area to solve various problems and classification is one of main problem in the field. The research describes algorithmic discussion of J48, J48 Graft, Random tree, REP, LAD. Here used to compare the performance of computing time, correctly classified instances, kappa statistics, MAE, RMSE, RAE, RRSE and to find the error rate measurement for different classifiers in weka .In this paper the data classification is diabetic patients data set is developed by collecting data from hospital repository consists of 1865 instances with different attributes. The instances in the dataset are two categories of blood tests, urine tests. Weka tool is used to classify the data is evaluated using 10 fold cross validation and the results are compared. When the performance of algorithms, we found J48 is better algorithm in most of the cases.
Uploads
Papers by Mrs P. Yasodha