Classification is an important problem in data mining Given a database of records, each with a class label, a classifier generates a concise and meaningful description for each class that can· be used to classify future records whose classes are unknown. A number of populat classifier exists to construct decision tree and to generate class model. This Classifier first builds decision tree and then prunes the subtrees from the decision tree in a subsequent pruning phase to improve accuracy and prevent overiitting. After the prunining the number of nodes is if very large for large data set, it takes more time to classify the data. Hence, the time component is one the most important factors of real world dataset. Each data has the specific time period in which the fact is true. It is called valid time. The present proposes to extend and improve the existing SPRINT algorithm by incorporating the temporal aspects. This classifier has the several advantages over the existing onces. It reduces the size of dataset by partitioning the data into different intervals. As a resultant, it takes less space. It acts as a faster classifier, which decreases time complexity. A chronological development ofclassifier is displayed at the end.
References not available.
Cite this article:
Verma and Vyas (2005). Concept of Decision Tree Classifier for Temporal data. Journal of Ravishankar University (Part-B: Science), 18(1), pp.07-22.