Multiresolution Knowledge Mining using Wavelet Transform

— Most research in Knowledge Mining deal with the basic models like clustering, classification, regression, association rule mining and so on. In the process of quest for knowledge most of the knowledge mining algorithms end up in generating global knowledge while losing focus on the local knowledge. This happens oftenly due to two reasons. First reason is due to dimensionality reduction. The problem of dimensionality reduction has been viewed as the reduction of features to the maximum extent possible while being able to retain the information conveyed by the data set. But most of the dimensionality reduction techniques reduce the dimensions keeping only the retention of global knowledge in mind while compromising with the loss of local knowledge. Second reason is due to optimized feature selection for making a global classification while not being bothered about the intra class relationship. In this paper we present methodologies using wavelet transform for overcoming the loss of l...

• Type: Journal
• Year: 2007
• Where: ENGL
• Authors: R. Pradeep Kumar, P. Nagabhushan
• Keywords: Dimensionality Reduction, ENGL 2007, Global Knowledge, Local Knowledge