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In many real life situations end results and basic starting data are known. To deduce conclusive evidence or to build holistic picture one needs to find out hidden information and missing text. This research paper delivers a novel algorithm (Probabilistic Intent-Action Ontology and Tone Matching Algorithm) to map multiple events on time line by determining their interdependency to predict the most...
Advanced satellite tracking technologies have collected huge amounts of wild bird migration data. Biologists use these data to understand dynamic migration patterns, study correlations between habitats, and predict global spreading trends of avian influenza. The research discussed here transforms the biological problem into a machine learning problem by converting wild bird migratory paths into graphs...
Grid computing systems are extremely large and complex so, manually dealing with its failures becomes impractical. Recently, it has been proposed that the systems themselves should manage their own failures or malfunctions. This is referred as self-healing. To deal with this challenging, is required to predict and control the process through a number of automated learning and proactive actions. In...
Since data mining problems contain a large amount of data, sampling is a necessity for the success of the task. Decision trees have been developed for prediction, and finding decision trees with smaller error rates has been a major task for their success. This paper suggests a structural sampling technique that is based on a generated decision tree, where the tree is generated based on fast and dirty...
A new predictive modelling approach known as associative classification, integrating association mining and classification into single system is being discussed as a better alternative for predictive analytics. Our paper investigates the performance issues of significant associative classifiers likes CMAR and CPAR. Performance comparisons observe that CPAR achieves improved performance as compared...
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