Data mining algorithms play an important role in the prediction of early-stage breast cancer. In this paper, we propose an approach that improves the accuracy and enhances the performance of three different classifiers: Decision Tree (J48), Naïve Bayes (NB), and Sequential Minimal Optimization (SMO). We also validate and compare the classifiers on two benchmark datasets: Wisconsin Breast Cancer (WBC) and Breast Cancer dataset. Data with imbalanced classes are a big problem in the classification phase since the probability of instances belonging to the majority class is significantly high, the algorithms are much more likely to classify new observations to the majority class. We address such problem in this work. We use the data level approach which consists of resampling the data in order to mitigate the effect caused by class imbalance. For evaluation, 10 fold cross-validation is performed. The efficiency of each classifier is assessed in terms of true positive, false positive, Roc curve, standard deviation (Std), and accuracy (AC). Experiments show that using a resample filter enhances the classifier’s performance where SMO outperforms others in the WBC dataset and J48 is superior to others in the Breast Cancer dataset.
The importance of rare itemset mining stems from its ability to discover unseen knowledge from datasets in real-life domains, such as identifying network failures, or suspicious behavior. There are significant efforts proposed to extract rare itemsets. The RP-growth algorithm outperforms previous methods proposed for generating rare itemsets. However, the performance of the RP-growth degrades on sparse datasets, and it is costly in terms of time and memory consumption. Hence, in this paper, we propose the RPP algorithm to extract rare itemsets. The advantage of the RPP algorithm is that it avoids time for generating useless candidate itemsets by omitting conditional trees as RP-growth does. Furthermore, our RPP algorithm uses a novel data structure, RN-list, for creating rare itemsets. To evaluate the performance of the proposed method, we conduct extensive experiments on sparse and dense datasets. The results show that the RPP algorithm is around an order of magnitude better than the RP-growth algorithm.
Data mining is the process of extracting useful unknown knowledge from large datasets. Frequent itemset mining is the fundamental task of data mining that aims at discovering interesting itemsets that frequently appear together in a dataset. However, mining infrequent (rare) itemsets may be more interesting in many real-life applications such as predicting telecommunication equipment failures, genetics, medical diagnosis, or anomaly detection. In this paper, we survey up-to-date methods of rare itemset mining. The main goal of this survey is to provide a comprehensive overview of the state-of-the-art algorithms of rare itemset mining and its applications. The main contributions of this survey can be summarized as follows. In the first part, we define the task of rare itemset mining by explaining key concepts and terminology, motivation examples, and comparisons with underlying concepts. Then, we highlight the state-of-art methods for rare itemsets mining. Furthermore, we present variations of the task of rare itemset mining to discuss limitations of traditional rare itemset mining algorithms. After that, we highlight the fundamental applications of rare itemset mining. In the last, we point out research opportunities and challenges for rare itemset mining for future research.
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