Publication 2002 Issue No. 6 - June Abstract - Density-Based Multiscale Data Condensation
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Density-Based Multiscale Data Condensation
June 2002 (vol. 24 no. 6)
pp. 734-747
 ASCII Text x Pabitra Mitra, C.A. Murthy, Sankar K. Pal, "Density-Based Multiscale Data Condensation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 6, pp. 734-747, June, 2002.
 BibTex x @article{ 10.1109/TPAMI.2002.1008381,author = {Pabitra Mitra and C.A. Murthy and Sankar K. Pal},title = {Density-Based Multiscale Data Condensation},journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},volume = {24},number = {6},issn = {0162-8828},year = {2002},pages = {734-747},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 - JOURJO - IEEE Transactions on Pattern Analysis and Machine IntelligenceTI - Density-Based Multiscale Data CondensationIS - 6SN - 0162-8828SP734EP747EPD - 734-747A1 - Pabitra Mitra, A1 - C.A. Murthy, A1 - Sankar K. Pal, PY - 2002KW - Data miningKW - multiscale condensationKW - scalabilityKW - density estimationKW - convergence in probabilityKW - instance learning.VL - 24JA - IEEE Transactions on Pattern Analysis and Machine IntelligenceER -

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 density-based 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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Index Terms:
Data mining, multiscale condensation, scalability, density estimation, convergence in probability, instance learning.
Citation:
Pabitra Mitra, C.A. Murthy, Sankar K. Pal, "Density-Based Multiscale Data Condensation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 6, pp. 734-747, June 2002, doi:10.1109/TPAMI.2002.1008381