Partition: Rectangular Data Table
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Bilinear clustering for mixed — quantitative, nominal and binary — variables is proved to be a theory-motivated extension of K-Means method.
Decomposition of the data scatter into “explained” and “residual” parts is provided (for each of the two norms: sum of squares and moduli).
Contribution weights are derived to attack machine learning problems (conceptual description, selecting and transforming the variables, and knowledge discovery).
The explained data scatter parts related to nominal variables appear to coincide with the chi-squared Pearson coefficient and some other popular indices, as well.
Approximation (bi)-partitioning for contingency tables substantiates and extends some popular clustering techniques.
KeywordsFuzzy Cluster Data Scatter Precision Error Cluster Partition Bilinear Model
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