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Pabitra Mitra, C.A. Murthy, Sankar K. Pal, "DensityBased Multiscale Data Condensation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 6, pp. 734747, June, 2002.  
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@article{ 10.1109/TPAMI.2002.1008381, author = {Pabitra Mitra and C.A. Murthy and Sankar K. Pal}, title = {DensityBased Multiscale Data Condensation}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {24}, number = {6}, issn = {01628828}, year = {2002}, pages = {734747}, doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2002.1008381}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  DensityBased Multiscale Data Condensation IS  6 SN  01628828 SP734 EP747 EPD  734747 A1  Pabitra Mitra, A1  C.A. Murthy, A1  Sankar K. Pal, PY  2002 KW  Data mining KW  multiscale condensation KW  scalability KW  density estimation KW  convergence in probability KW  instance learning. VL  24 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
A problem gaining interest in pattern recognition applied to data mining is that of selecting a small representative subset from a very large data set. In this article, a nonparametric data reduction scheme is suggested. It attempts to represent the density underlying the data. The algorithm selects representative points in a multiscale fashion which is novel from existing densitybased approaches. The accuracy of representation by the condensed set is measured in terms of the error in density estimates of the original and reduced sets. Experimental studies on several real life data sets show that the multiscale approach is superior to several related condensation methods both in terms of condensation ratio and estimation error. The condensed set obtained was also experimentally shown to be effective for some important data mining tasks like classification, clustering, and rule generation on large data sets. Moreover, it is empirically found that the algorithm is efficient in terms of sample complexity.
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