D-IMPACT: A Data Preprocessing Algorithm to Improve the Performance of Clustering
| Autor: | Tu Kien T. Le, Kenji Satou, Vu Anh Tran, Mamoru Kubo, Xuan Tho Dang, Osamu Hirose, Thammakorn Saethang, Duc Luu Ngo, Gavrilov Sergey, Lan Anh T. Nguyen, Yoichi Yamada |
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| Rok vydání: | 2014 |
| Předmět: |
DBSCAN
Fuzzy clustering business.industry Computer science Correlation clustering Pattern recognition computer.software_genre Determining the number of clusters in a data set ComputingMethodologies_PATTERNRECOGNITION Data stream clustering CURE data clustering algorithm Canopy clustering algorithm Artificial intelligence Data mining business Cluster analysis Algorithm computer |
| Zdroj: | Journal of Software Engineering and Applications. :639-654 |
| ISSN: | 1945-3124 1945-3116 |
| DOI: | 10.4236/jsea.2014.78059 |
| Popis: | In this study, we propose a data preprocessing algorithm called D-IMPACT inspired by the IMPACT clustering algorithm. D-IMPACT iteratively moves data points based on attraction and density to detect and remove noise and outliers, and separate clusters. Our experimental results on two-dimensional datasets and practical datasets show that this algorithm can produce new datasets such that the performance of the clustering algorithm is improved. |
| Databáze: | OpenAIRE |
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