An intention of MapReduce Sets for Summarization expressions analysis has to suggest criteria how summarization expressions in summarization data can be defined in a meaningful way and how they should be compared. Similitude based MapReduce Sets for summarization Expression Analysis and MapReduce Sets for Assignment is expected to adhere to fundamental principles of the scientific summarization process that are expressiveness of summarization models and reproducibility of their summarization inference. Summarization expressions are assumed to be elements of a summarization expression space or Conjecture class and summarization data provide "information" which of these summarization expressions should be used to interpret the summarization data. An inference summarization algorithm constructs the mapping between summarization data and summarization expressions, in particular by a summarization cost minimization process. Fluctuations in the summarization data often limit the summarization precision, which we can achieve to uniquely identify a single summarization expression as interpretation of the summarization data. We advocate an information theoretic perspective on summarization expression analysis to resolve this dilemma where the tradeoff between summarization informativeness of statistical inference summarization and their summarization stability is mirrored in the information-theoretic summarization optimum of high summarization information rate and zero communication expression error. The inference summarization algorithm is considered as an outlier object summarization path, which naturally limits the resolution of the summarization expression space given the uncertainty of the summarization data.

MapReduce summarization expressions kernel function.