A normalization database is considered as mathematics logical design to avoid redundancies and inconsistencies in the stored data which brought about null data and many relationship tables in the physical database. A retrieval rate is affected directly in running a practical system of the normalization design. We argue in the paper that rate formulas given out retrieval speed via testing the vast amount of data with decomposing fields and data in practical or ideal network environment. The rate formulas show that relationship tables increased must decrease retrieval speed and a minimum table must increase retrieval speed on the contrary, which compared with normal form according to the database theory and practical requirement. A speed is predictable to calculate the rate formulas. The retrieval speed is mainly concerned with server performance base on normal form too.
This paper presents a theoretical explanation and test results that an amount of information would not increase infinitely in transmission via the mathematical model of the matter for deciphering original works of information entropy and the principle of thermodynamic entropy. The rate formula with energy is derived via retrieval speed testing the vast amount of data in DBMS and variables in rate formula proved via other testing methods. We found that the actual value in the rate formula was quite different from the theoretical value that calculating the entropy per bit of information needed energy and the speed need energy. We used of testing data to illustrate these relations between bit and energy, 1 bit =3.92×10-4 J/K. We assumed that the energy of Brillouin's information entropy was the surface entropy changes between information source and information channel rather than the information source itself.
An improved clustering algorithm was presented based on density-isoline clustering algorithm. The new algorithm can do a better job than density-isoline clustering when dealing with noise, not having to literately calculate the cluster centers for the samples batching into clusters instead of one by one. After repeated experiments, the results demonstrate that the improved density-isoline clustering algorithm is significantly more efficiency in clustering with noises and overcomes the drawbacks that traditional algorithm DILC deals with noise and that the efficiency of running time is improved greatly.
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