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Rough Fuzzy Clustering Using Decision Theory: a Data Mining Approach Ammisetty Veeraswamy
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Rough Fuzzy Clustering Using Decision Theory: a Data Mining Approach
Ammisetty Veeraswamy
Clustering is the unsupervised classification of patterns into groups. In non-fuzzy or hard clustering, data is divided into crisp clusters, where each data point belongs to exactly one cluster. In fuzzy clustering, the data points can belong to more than one cluster, and associated with each of the points are membership grades which indicate the degree to which the data points belong to the different clusters. Different clustering algorithms produce clusters with different characteristics. In this Book specifies the Clustering techniques using decision theory.
| Media | Böcker Pocketbok (Bok med mjukt omslag och limmad rygg) |
| Releasedatum | 8 november 2012 |
| ISBN13 | 9783659297205 |
| Utgivare | LAP LAMBERT Academic Publishing |
| Antal sidor | 100 |
| Mått | 150 × 6 × 226 mm · 167 g |
| Språk | Tyska |
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