N-Nodeset Importance Representative Based Outlier Detection for Categorical Data

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Provided by: International Journal Of Engineering And Computer Science
Topic: Data Management
Format: PDF
The proportionate increase in the size of the data with increase in space implies that clustering and hence outlier detection a very large data set becomes difficult and is a time consuming process. Sampling is one important technique to scale down the size of dataset and to improve the efficiency of clustering. After sampling, allocating unlabeled objects into proper clusters is impossible in the categorical domain. To address the problem, chen employed a method called MARDL to allocate each unlabeled data point to the appropriate cluster based on NIR (Node Importance Representative) and NNIR (N-Nodeset Importance Representative) algorithms.
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