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Please use this identifier to cite or link to this item: http://eprint.iitd.ac.in/handle/2074/1932

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dc.contributor.authorGupta, S K-
dc.contributor.authorSomayajulu, D V L N-
dc.contributor.authorArora, J K-
dc.contributor.authorVasudha, B-
dc.date.accessioned2006-07-03T04:51:20Z-
dc.date.available2006-07-03T04:51:20Z-
dc.date.issued1998-
dc.identifier.citationDatabase and Expert Systems Applications, Proceedings Ninth International Workshop on, 246 - 251p.en
dc.identifier.urihttp://eprint.iitd.ac.in/dspace/handle/2074/1932-
dc.description.abstractThe paper presents an algorithm to solve the problem of classification for data mining applications. This is a decision tree classifier which uses modified gini index as the partitioning criteria. A pre-sorting technique is used to overcome the problem of sorting at each node of the tree. This technique is integrated with a breadth first tree growth strategy which enables us to calculate the best partition for each of the leaf nodes in a single scan of a database. We have implemented this algorithm using depth first tree growth strategy also. The algorithm uses a dynamic pruning approach which reduces the number of scans of the database and does away with a separate tree pruning phase. The proof of correctness, analysis and performance study are also presenteden
dc.format.extent64569 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoenen
dc.subjectdata mining applicationsen
dc.subjectdynamic pruningen
dc.titleScalable classifiers with dynamic pruningen
dc.typeArticleen
Appears in Collections:Computer Science and Engineering

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