The method of processing two algorithms within a single workflow, and hence the combined method, is called as hybrid computing. We propose a data mining framework comprising of two stages, namely clustering and classification. The first stage employs k-means algorithm on data and generates two clusters, namely cluster-0 and cluster-1. Instances in cluster-0 do not have disease symptoms and cluster-1 consists of instances with disease symptoms. The verification of valid grouping is then carried out by referring to the association of class labels in original datasets. Incorrectly classified instances are removed and remaining instances are used to build the classifier using C4.5 decision-tree algorithm with k-fold cross validation method. The framework was tested using eight datasets from the machine learning repository of the UCI. The proposed framework was evaluated for accuracy, sensitivity and specificity measures. Our framework obtained promising classification accuracy as compared to other methods found in the literature.
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- Effective framework for prediction of disease outcome using ...
Learn to Diagnose Problems in Your Trees, Including New Beech Disease
A mystery illness is hitting northeast Ohio’s American beech trees. Called beech leaf disease, it’s causing striped and curled leaves, weak buds, and sometimes the death of saplings. It seems to be spreading fast, too. “And we really don’t know what’s causing it,” said Kathy Smith, forestry program coordinator at The Ohio State University. The less-bad news: An upcoming event will shed light on the disease, plus many other problems that can bug Ohio’s trees. Diseases,...
What is it about this soil that protects plants from devastating disease?
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Maxum process gas chromatograph measures and optimizes synthetic rubber production – Case Study
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