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| Granulares Rechnen (Informationsgranulation)× | K-Means-Clustering× | |
|---|---|---|
| Fachgebiet≠ | Soft Computing | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1997 | 1967 |
| Urheber≠ | Lotfi A. Zadeh (information granulation); developed by Pedrycz, Skowron, Yao | MacQueen, J. |
| Typ≠ | Framework for multi-granularity information processing | Partitional clustering (centroid-based) |
| Wegweisende Quelle≠ | Zadeh, L. A. (1997). Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems, 90(2), 111–127. DOI ↗ | MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297. link ↗ |
| Aliasnamen | information granulation, computing with granules, three-way granular computing, tanecikli hesaplama | K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering |
| Verwandt | 3 | 3 |
| Zusammenfassung≠ | Granular computing is a problem-solving paradigm that processes information in 'granules' — clumps of objects drawn together by indistinguishability, similarity, or functionality — rather than at the level of individual data points. Articulated by Lotfi Zadeh in 1997 as fuzzy information granulation and developed into a broad framework, it provides a unifying umbrella over fuzzy sets, rough sets, and interval methods, letting analysis move to whichever level of detail a problem actually requires. | K-Means Clustering is a centroid-based partitional clustering algorithm, traced to J. MacQueen in 1967, that splits data into k clusters by assigning each observation to its nearest cluster centre. It is widely used for marketing segmentation, customer grouping, and exploratory analysis. |
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