Clustering for Data Mining: A Data Recovery ApproachCRC Press, 2005 M04 29 - 296 páginas Often considered more as an art than a science, the field of clustering has been dominated by learning through examples and by techniques chosen almost through trial-and-error. Even the most popular clustering methods--K-Means for partitioning the data set and Ward's method for hierarchical clustering--have lacked the theoretical attention that wou |
Contenido
1 What Is Clustering | 1 |
2 What is Data | 37 |
3 KMeans Clustering | 75 |
4 Ward Hierarchical Clustering | 111 |
5 Data Recovery Models | 137 |
6 Different Clustering Approaches | 177 |
7 General Issues | 207 |
Data Recovery Approach in Clustering | 245 |
Bibliography | 249 |
| 261 | |
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according agglomerative analysis Anomalous pattern APPCOD applied average binary features categorical features centroids chi-square classication cluster centroids cluster structure clustering algorithm computational conceptual clustering conceptual description considered contingency table contribution correlation ratio corresponding criterion data matrix data mining data recovery approach data recovery model data scatter data set data table decision tree decomposition dened denition denote described dierent dissimilarity distribution divisive clustering entity set entries equation Euclidean distance Example feature space Figure function gene grand mean inner product iteration K-Means machine learning Masterpieces data maximizing minimizing Minimum distance rule minimum spanning tree nding nodes number of clusters number of entities partition pre-specied principal components quantitative Quetelet coecients reect referred rescaling scale self-organizing maps similarity matrix species split square error standard deviation statistical independence statistics subset Total upper cluster hierarchy values variables variance vectors Ward distance weight