BRIM: An Efficient Boundary Points Detecting Algorithm

Bao-zhi, Qiu; Yue, Feng; Shen, Jing

doi:10.1007/978-3-540-71701-0_83

Cited by 29 publications

(19 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on the basic aiNet algorithm, Li Jie et al introduced the concept of taboo cloning in immunology to the artificial immune network clustering algorithm, which solved the problem that aiNet cannot handle the fuzzy boundary of the sample subset [29]. Considering the problem of memory network dynamics and irregular changes caused by the lack of objective function guidance of the aiNet algorithm, Guo Jianhua et al established the overall objectives and constraints of the memory network by defining quality evaluation standards, thus realizing the guidance of the algorithm, and discussed the value of the compression threshold [30]. To overcome the problem that the monoclonal algorithm easily falls into local optimum, Zhou Yang et al proposed an evolutionary immune network clustering algorithm based on polyclonal algorithms [31].…”

Section: Related Workmentioning

confidence: 99%

Artificial immune network clustering based on a cultural algorithm

Deng

Yang

Liu

2020

J Wireless Com Network

View full text Add to dashboard Cite

Data mining technology has been applied in many fields. Prototype-based cluster analysis is an important data mining method, but its ability to discover knowledge is limited because of the need to know the number of target data categories and cluster prototypes in advance. Artificial immune evolutionary network clustering is a clustering method based on network structure. Compared with prototype-based cluster analysis, it has the advantage of realizing unsupervised learning and clustering without any prior knowledge of data. However, artificial immune evolutionary network clustering also has problems such as a lack of guidance in the clustering process, fuzzy boundary sensitivity, and difficulty in determining parameters. To solve these problems, an artificial immune network clustering algorithm based on a cultural algorithm is proposed. First, three kinds of knowledge are constructed: normative knowledge is used to regulate the spatial range of population initialization to avoid blindness; state knowledge is used to distinguish the type of antigen, and immune defense measures are taken to prevent the network structure caused by noise and boundaries from being unclear; and topology knowledge is used to guide the antigen for optimal antibody search. Second, topology knowledge in the cultural algorithm is used to characterize the distribution of antigens and antibodies in space, and elite learning is used to improve the traditional clone mutation operator. Based on the shadow set theory, a method for adaptively determining the compression threshold is proposed. Finally, the results of simulation experiments show that the proposed algorithm can effectively overcome the above problems, and the clustering performances on a synthetic dataset and an actual dataset are satisfactory.

show abstract

Section: Related Workmentioning

confidence: 99%

Artificial immune network clustering based on a cultural algorithm

Deng

Yang

Liu

2020

J Wireless Com Network

View full text Add to dashboard Cite

show abstract

“…For higher dimensional manifolds, algorithms of the second class are appropriate. These methods iterate through each data point and use a set of parameters to determine whether on not they lie on the boundary [89][90][91][92].…”

Section: Boundary Detectionmentioning

confidence: 99%

An Exploration Algorithm for Stochastic Simulators Driven by Energy Gradients

Georgiou

Bello-Rivas

Gear

et al. 2017

Entropy

View full text Add to dashboard Cite

In recent work, we have illustrated the construction of an exploration geometry on free energy surfaces: the adaptive computer-assisted discovery of an approximate low-dimensional manifold on which the effective dynamics of the system evolves. Constructing such an exploration geometry involves geometry-biased sampling (through both appropriately-initialized unbiased molecular dynamics and through restraining potentials) and, machine learning techniques to organize the intrinsic geometry of the data resulting from the sampling (in particular, diffusion maps, possibly enhanced through the appropriate Mahalanobis-type metric). In this contribution, we detail a method for exploring the conformational space of a stochastic gradient system whose effective free energy surface depends on a smaller number of degrees of freedom than the dimension of the phase space. Our approach comprises two steps. First, we study the local geometry of the free energy landscape using diffusion maps on samples computed through stochastic dynamics. This allows us to automatically identify the relevant coarse variables. Next, we use the information garnered in the previous step to construct a new set of initial conditions for subsequent trajectories. These initial conditions are computed so as to explore the accessible conformational space more efficiently than by continuing the previous, unbiased simulations. We showcase this method on a representative test system.

show abstract

“…The algorithm is called cultural evolutionary artificial immune network (CaiNet). The algorithm uses the topological knowledge in the cultural algorithm to characterize the antigens and antibodies in the space [25][26][27][28][29][30][31][32] . When the antigen searches for the antibody with the highest affinity, it searches through the antibodies in the topological unit where it is located.…”

Section: Introductionmentioning

confidence: 99%

Artificial Immune Network Clustering Based on a Cultural Algorithm

Deng

Yang

Liu

2020

Preprint

View full text Add to dashboard Cite

There are some problems in evolutionary immune network clustering, such as the lack of guidance in the clustering process, the sensitivity of the fuzzy boundary and the difficulty in determining parameters. To solve these problems, an artificial immune network clustering algorithm based on a cultural algorithm is proposed. Three kinds of knowledge are constructed: normative knowledge is used to standardize the spatial scope of population initialization, avoiding blindness; state knowledge is used to distinguish antigens and take immune defense measures to prevent noise and unclear network structure caused by boundary; topology knowledge is used to guide the optimal antibody search. The clone mutation operation of the traditional method is improved, and a compression threshold adaptive determination method is proposed based on the shadow sets theory. The experimental results show that the proposed method can effectively overcome the above problems, and the clustering performance on a synthetic dataset and an actual dataset is satisfactory.

show abstract

BRIM: An Efficient Boundary Points Detecting Algorithm

Cited by 29 publications

References 3 publications

Artificial immune network clustering based on a cultural algorithm

Artificial immune network clustering based on a cultural algorithm

An Exploration Algorithm for Stochastic Simulators Driven by Energy Gradients

Artificial Immune Network Clustering Based on a Cultural Algorithm

Contact Info

Product

Resources

About