A computational method utilizing divide-and-conquer and incremental computation was proposed to calculate the structure of attribute granule of an inseparable information system. Firstly, the rule that how the structure of attribute granule of an information system changed when new Functional Dependency (FD) was added to the functional dependency set of an information system was studied and the increment theorem of information system structure was proved. Secondly, by removing a part of the functional dependency, an inseparable information system could become a separable information system and the structure of the separable information system was calculated by using decomposition theorem. Thirdly, the removed functional dependency was added to the separable information system and the structure of the original information system was calculated by using increment theorem. Lastly, the algorithm to calculate the structure of attribute granule of inseparable information system was given and its complexity was analyzed. The complexity of the direct calculation of the structure of attribute granule of information system was O(n×m×2ⁿ), and the proposed method could reduce the complexity to below O(n×k×2ⁿ)(k<m), and when k=1,2, the complexity could be reduced to O(n₁×m₁×2^n₁)+O(n₂×m₂×2^n₂)(n=n₁+n₂,m=m₁+m₂). The theoretical analysis and practical calculation demonstrate that the proposed method can effectively reduce the computational complexity of the structure of attribute granule of an inseparable information system.

Based on functional dependency over the attributes, the concept of attribute information granule of information system was proposed, and a method to calculate the structure of attribute granule of separable information system was given. Firstly, the separability of information system was defined, and it was proved that if an information system is separable, the structure of attribute granule of the system can be decomposed into the Cartesian product of the structures of attribute granules of its sub-systems. Secondly, the method to judge the separability of an information system and the decomposition algorithm of information system were given. Lastly, the complexity of the proposed method was analyzed. And the analysis result demonstrates that the complexity of the direct calculation of the structure of attribute granule of information system is O(2ⁿ), and the proposed method can reduce it to O(2^n₁+2^n₂+…+2^n_k) where n=n₁+n₂+…+n_k. The theoretical analysis and example show that the method is feasible.

The research of text similarity analysis and text clustering is mostly based on feature words. Because Chinese text does not have a natural delimiter between words, it must solve two problems such as Chinese word segmentation and higher-level dimensions feature vector spaces. In order to reduce the higher complexity, a novel investigation method of text similarity analysis using the association of Chinese characters was probed without using feature words. The notation of Chinese Character Association Measurement was defined， and the Chinese Character Association Measurement matrix to represent the Chinese text documents was constructed. Then a Chinese text similarity algorithm based on Chinese Character Association Measurement Matrix is proposed. The experiment result shows the Chinese Character Association Measurement is better than the mutual information and the T test and the bi-gram frequency. Without Chinese word segmentation, so this algorithm is useful in massive Chinese data corpus.