Name
Abstract | ||
|---|---|---|
Abstract.,Classification,is an important,problem,in the emerging,field of data,mining.,Although classification has been studied extensively in the past, most of the classification algorithms are designed only for memory-resident data, thus limiting their suitability for data mining large,data,sets. This paper,discusses,issues in building,a scalable,classifier and presents the design of SLIQ’ , a new,classifier. SLIQ is a decision tree classifier that can handle both numeric and categorical attributes. It uses a novel,pre-sorting,technique,in the tree-growth,phase. This sorting,procedure,is integrated,with,a breadth-fist,tree growing,strategy to enable,classification,of disk-resident,datasets. SLIQ also uses,a new tree-pruning algorithm that is inexpensive, and results in compact aad accurate,trees. The combination,of these techniques,enables SLIQ to scale for lerge,data,sets and classify data sets irrespective,of the,number,of classes, attributes, and examples (records), thus making it an attractive tooldata,mining. |
Year | DOI | Venue |
|---|---|---|
1996 | 10.1007/BFb0014141 | International Conference on Extending Database Technology |
Keywords | Field | DocType |
fast scalable classifier,data mining,decision tree classifier | Decision tree,Data mining,Computer science,Categorical variable,Tree (data structure),C4.5 algorithm,Artificial intelligence,Classifier (linguistics),Statistical classification,Machine learning,Decision tree learning,Incremental decision tree | Conference |
ISBN | Citations | PageRank |
3-540-61057-X | 322 | 116.95 |
References | Authors | |
20 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manish Mehta | 1 | 866 | 312.14 |
| Rakesh Agrawal | 2 | 29751 | 5959.33 |
| Jorma Rissanen | 3 | 1665 | 798.14 |