Unsupervised data reduction
| Autor: | Oleg Okun, Helen Priisalu |
|---|---|
| Rok vydání: | 2007 |
| Předmět: |
Class (set theory)
Fuzzy clustering business.industry Centroid Pattern recognition computer.software_genre Matrix decomposition Data set Set (abstract data type) Exploratory data analysis ComputingMethodologies_PATTERNRECOGNITION Control and Systems Engineering Signal Processing Computer Vision and Pattern Recognition Data mining Artificial intelligence Electrical and Electronic Engineering business computer Software Data reduction Mathematics |
| Zdroj: | Signal Processing. 87:2260-2267 |
| ISSN: | 0165-1684 |
| DOI: | 10.1016/j.sigpro.2007.02.006 |
| Popis: | We propose a data reduction method based on fuzzy clustering and nonnegative matrix factorisation. In contrast to different variants of data set editing typically used for data reduction, our method is completely unsupervised, i.e., it does not need class labels to eliminate examples from a data set. Thus, it is useful in exploratory data analysis when class labels of examples are unknown or unavailable in order to gain insight into structure of different groups of patterns. Also unlike many types of unsupervised clustering relating a single example (cluster centroid) to each cluster, our method associates a set of the most representative examples with each cluster. Hence, it makes cluster structure more transparent to a data analyst. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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