Frequent itemset mining is the most important step of association rule mining. It plays a very important role in incremental data environments. The massive volume of data creates an imminent need to design incremental algorithms for the maximal frequent itemset mining in order to handle incremental data over time. In this study, we propose an incremental maximal frequent itemset mining algorithms that integrate subjective interestingness criterion during the process of mining. The proposed framework is designed to deal with incremental data, which usually come at different times. It extends FP-Max algorithm, which is based on FP-Growth method by pushing interesting measures during maximal frequent itemset mining, and performs dynamic and early pruning to leave uninteresting frequent itemsets in order to avoid uninteresting rule generation. The framework was implemented and tested on public databases, and the results found are promising.
In this paper, the technique is propose that made use of Particle Swarm Optimization (PSO) algorithm for tumor detection by using the techniques of Image Processing. This proposed algorithm is based on three steps. First, it identities the affected area, Second, it makes enhancement to the image, Finally, it performs segmentation and extraction of characteristics of the affected area. The propose approach takes any medical image of Computed Tomography CT scan, and provides indicators for physicians decision-making to build treatment plans with minimal diagnostic errors and more accurate description of the treatment plan at minimal cost. The proposed approach has been implemented and tested using data from Oncology Center (the National Center for Oncology Therapy, Hadramout El Wadi, Yemen) and shown very promising .
Association rules are an important problem in data mining. Massively increasing volume of data with temporal dependencies in real life databases has motivated researchers to design novel and incremental algorithms for temporal association rules mining. In this paper, an incremental association rules mining algorithm is proposed that integrates interestingness criterion during the process of building the model called SUMA. One of the main features of the proposed framework is to capture the user background knowledge, which is monotonically augmented. The incremental model that reflects the changing data over the time and the user beliefs is attractive in order to make the over all KDD process more effective and efficient. The proposed framework is implemented and experiment it with some public datasets and found the results quite promising.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.