列序聚类法和最小变化拟合在体育选材中的应用

本文应用列序聚类法和最小变化拟合于运动员选材。介绍了这种方法的基本步骤,给出了计算程序和得到解的迭代过程。结果表明,这种方法在运动员选材中是非常有效的。

奇异向量空间双聚类算法

本文针对0/1矩阵的双聚类问题提出一种奇异向量空间双聚类算法.通过SVD分解将0/1矩阵映射到左右奇异向量空间上,然后利用信息熵判断行聚类优先还是列聚类优先,最后根据判断结果递归进行行聚类或列聚类,直到满足停止条件.实验显示奇异向量空间双聚类算法可以分辨出完全无重叠的子矩阵,比较快速地得到硬的双簇.

Microarray-based analysis for hepatocellular carcinoma: From gene expression profiling to new challenges

Accumulation of mutations and alterations in the expression of various genes result in carcinogenesis,and the development of microarray technology has enabled us to identify the comprehensive gene expression alterations in oncogenesis.Many studies have applied this technology for hepatocellular carcinoma(HCC),and identified a number of candidate genes useful as biomarkers in cancer staging,prediction of recurrence and prognosis,and treatment selection.Some of these target molecules have been used to develop new serum diagnostic markers and therapeutic targets against HCC to benefit patients.Previously,we compared gene expression profiling data with classification based on clinicopathological features,such as hepatitis viral infection or liver cancer progression.The next era of gene expression analysis will require systematic integration of expression profiles with other types of biological information,such as genomic locus,gene function,and sequence information.We have reported integration between expression profiles and locus information,which is effective in detecting structural genomic abnormalities,such as chromosomal gains and losses,in which we showed that gene expression profiles are subject to chromosomal bias.Furthermore,array-based comparative genomic hybridization analysis and allelic dosage analysis using genotyping arrays for HCC were also reviewed,with comparison of conventional methods.

CLUTTER SUPPRESSION METHOD IN GPR USING PARTICLE CLUSTERING

In this paper, a novel clutter suppression method in Ground Penetrating Radar (GPR) is proposed. Time segments of hill are represented by their corresponding particle in B-scan. Those particles in B-scan are clustered to represent reflectors (such as buried targets, air-soil interface). The clusters of buried target have a particle sequence with single peak. Therefore, if the particles donot belong to the cluster of buried target, time segment they represent will be suppressed. Experimental results and simulation are provided to demonstrate that the new algorithm outperforms existing approaches.

Genomic Signal Enhancement by Clustering

Weight matrix models for signal sequence motif are simple. A main limitation of the models is the assumption of independence between positions. Signal enhancement is achieved by taking the total likelihood as the objective function for maximization to cluster sequences into groups with different patterns. As an example, the initial and terminal signals for translation in rice genome are examined.

Identifying Similar Operation Scenes for Busy Area Sector Dynamic Management

Air traffic controllers face challenging initiatives due to uncertainty in air traffic.One way to support their initiatives is to identify similar operation scenes.Based on the operation characteristics of typical busy area control airspace,an complexity measurement indicator system is established.We find that operation in area sector is characterized by aggregation and continuity,and that dimensionality and information redundancy reduction are feasible for dynamic operation data base on principle components.Using principle components,discrete features and time series features are constructed.Based on Gaussian kernel function,Euclidean distance and dynamic time warping(DTW)are used to measure the similarity of the features.Then the matrices of similarity are input in Spectral Clustering.The clustering results show that similar scenes of trend are not ideal and similar scenes of modes are good base on the indicator system.Finally,actual vertical operation decisions for area sector and results of identification are compared,which are visualized by metric multidimensional scaling(MDS)plots.We find that identification results can well reflect the operation at peak hours,but controllers make different decisions under the similar conditions before dawn.The compliance rate of busy operation mode and division decisions at peak hours is 96.7%.The results also show subjectivity of actual operation and objectivity of identification.In most scenes,we observe that similar air traffic activities provide regularity for initiatives,validating the potential of this approach for initiatives and other artificial intelligence support.

AVLINK： Robust Clustering Algorithm based on Average Link Applied to Protein Sequence Analysis

Robust Clustering methods are aimed at avoiding unsatisfactory results resulting from the presence of certain amount of outlying observations in the input data of many practical applications such as biological sequences analysis or gene expressions analysis. This paper presents a fuzzy clustering algorithm based on average link and possibilistic clustering paradigm termed as AVLINK. It minimizes the average dissimilarity between pairs of patterns within the same cluster and at the same time the size of a cluster is maximized by computing the zeros of the derivative of proposed objective function. AVLINK along with the proposed initialization procedure show a high outliers rejection capability as it makes their membership very low furthermore it does not requires the number of clusters to be known in advance and it can discover clusters of non convex shape. The effectiveness and robustness of the proposed algorithms have been demonstrated on different types of protein data sets.

Research on natural language recognition algorithm based on sample entropy

Sample entropy can reflect the change of level of new information in signal sequence as well as the size of the new information. Based on the sample entropy as the features of speech classification, the paper firstly extract the sample entropy of mixed signal, mean and variance to calculate each signal sample entropy, finally uses the K mean clustering to recognize. The simulation results show that： the recognition rate can be increased to 89.2% based on sample entropy.