Research on Granularity Pair and It’s Related Properties
0
Citation
6
Reference
10
Related Paper
Keywords:
Granularity
The information entropy between coarse granularity and fine granularity is comparatively studied,the influence on decision tree caused by coarse granularity and fine granularity is investigated,and the conclusion is provided that the information entropy under coarse granularity is not less than the one under fine granularity.It is shown that the decision tree generated by selecting the expanded attribute under fine granularity is better than the one under coarse granularity.
Granularity
Cite
Citations (1)
Granularity
Cite
Citations (9)
Granularity
Granular Computing
Cite
Citations (13)
Granularity
Lattice (music)
Cite
Citations (22)
This paper discusses a mining problem of approximate periodicity with multi-granularity time in the temporal database. It introduces the concepts and properties of the multi-granularity time interval on the basis of multi-granularity time and multi-granularity time format. It constructs multi-granularity approximate periodic pattern. It proposes an mining algorithm based on self-organizing map to find multi-granularity approximate periodic pattern. Results obtained from experiments on high frequency stock market data of 580000 Bao Steel JBT1 demonstrate that the proposed algorithm is efficient.
Granularity
Temporal database
Basis (linear algebra)
Cite
Citations (0)
The granularity of electrophoretic developed images is discussed. The monolayer model which fits very well for powder cloud development is not adequate for electrophoretic developed images. Preference should be given to the multi-layer model which follows the Siedentopf relationship.A lowering of the granularity level by fix is observed.A comparison has been made of calculated and observed particle diameters.
Granularity
Particle (ecology)
Cite
Citations (1)
The security of many cryptographic constructions relies on assumptions related to Discrete Logarithms (DL), e.g., the Diffie-Hellman, Square Exponent, Inverse Exponent or Representation Problem assumptions. In the concrete formalizations of these assumptions one has some degrees of freedom offered by parameters such as computational model, the problem type (computational, decisional) or success probability of adversary. However, these parameters and their impact are often not properly considered or are simply overlooked in the existing literature.
In this paper we identify parameters relevant to cryptographic applications and describe a formal framework for defining DL-related assumptions. This enables us to precisely and systematically classify these assumptions.
In particular, we identify a parameter, termed granularity, which describes the underlying probability space in an assumption. Varying granularity we discover the following surprising result: We prove that two DL-related assumptions can be reduced to each other for medium granularity but we also show that they are provably not reducible with generic algorithms for high granularity. Further we show that reductions for medium granularity can achieve much better concrete security than equivalent high-granularity reductions.
Granularity
Discrete logarithm
Representation
Concrete security
Exponent
Cite
Citations (2)
Granularity
Concrete security
Discrete logarithm
Representation
Cite
Citations (57)
A new mathematical formula for colour granularity has recently been proposed by Saunders1. Whereas some previously derived mathematical expressions of colour granularity have proved difficult to apply in practice due to the existence of terms having no real physical correlate, this new formula relates granularity to easily measured film parameters. Using this formula, successful granularity predictions have been made for a series of single-laver colour negative structures in which the coupler silver halide ratio was systematically varied.
Granularity
Cite
Citations (1)
A data mining method, which discovers fuzzy rules in multiple granularity time series, was proposed. This method introduces a multiple granularity into time series and provides the outcome of data mining in the form of fuzzy rules. After the mathematical model of multiple granularity time series is established, some notations related to the rule discovering are defined. The mining algorithm is presented in details. The results of some experiments are also provided to indicate the validity of the mining algorithm.
Granularity
Cite
Citations (0)