弱智儿童识记材料的组织特点及训练的实验研究（一）

本研究设计了三个识记测验（数字组织测验、类群集测验、主观组织测验），对弱智儿童识记时记忆组织特点进行研究，结果表明，弱智儿童识记表现出以下特征：１．大多数弱智儿童采用机械识记法，记忆的数字组织水平很低，只在难度低的项目中表现出有记忆组织的能力，对类群集和无关联材料的记忆，没有记忆组织的能力。２．在对数字组织材料识记时，要借助于外部言语和手的动作，识记速度缓慢。３．识记时记忆监控能力很差。研究还表明：有无利用记忆组织识记是影响回忆量的一个重要因素。

A minimal axiom group for rough set based on quasi-ordering

Rough set axiomatization is one aspect of rough set study to characterize rough set theory using dependable and minimal axiom groups. Thus, rough set theory can be studied by logic and axiom system methods. The classic rough set theory is based on equivalent relation, but rough set theory based on reflexive and transitive relation (called quasi-ordering) has wide applications in the real world. To characterize topological rough set theory, an axiom group named RT, consisting of 4 axioms, is proposed. It is proved that the axiom group reliability in characterizing rough set theory based on similar relation is reasonable. Simultaneously, the minimization of the axiom group, which requires that each axiom is an equation and each is independent, is proved. The axiom group is helpful for researching rough set theory by logic and axiom system methods.

A new clustering algorithm for large datasets

The Circle algorithm was proposed for large datasets.The idea of the algorithm is to find a set of vertices that are close to each other and far from other vertices.This algorithm makes use of the connection between clustering aggregation and the problem of correlation clustering.The best deterministic approximation algorithm was provided for the variation of the correlation of clustering problem,and showed how sampling can be used to scale the algorithms for large datasets.An extensive empirical evaluation was given for the usefulness of the problem and the solutions.The results show that this method achieves more than 50% reduction in the running time without sacrificing the quality of the clustering.

Free clustering optimal particle probability hypothesis density(PHD) filter

As to the fact that it is difficult to obtain analytical form of optimal sampling density and tracking performance of standard particle probability hypothesis density(P-PHD) filter would decline when clustering algorithm is used to extract target states,a free clustering optimal P-PHD(FCO-P-PHD) filter is proposed.This method can lead to obtainment of analytical form of optimal sampling density of P-PHD filter and realization of optimal P-PHD filter without use of clustering algorithms in extraction target states.Besides,as sate extraction method in FCO-P-PHD filter is coupled with the process of obtaining analytical form for optimal sampling density,through decoupling process,a new single-sensor free clustering state extraction method is proposed.By combining this method with standard P-PHD filter,FC-P-PHD filter can be obtained,which significantly improves the tracking performance of P-PHD filter.In the end,the effectiveness of proposed algorithms and their advantages over other algorithms are validated through several simulation experiments.

Clustering Categorical Data:A Cluster Ensemble Approach

Clustering categorical data, an integral part of data mining,has attracted much attention recently. In this paper, the authors formally define the categorical data clustering problem as an optimization problem from the viewpoint of cluster ensemble, and apply cluster ensemble approach for clustering categorical data. Experimental results on real datasets show that better clustering accuracy can be obtained by comparing with existing categorical data clustering algorithms.

PSO type-reduction method for geometric interval type-2 fuzzy logic systems

In a special case of type-2 fuzzy logic systems （FLS）, i.e. geometric inteIval type-2 fuzzy logic systems （GIT-2FLS）, the crisp output is obtained by computing the geometric center of footprint of uncertainly （FOU） without type-reduction, but the defuzzifying method acts against the corner concepts of type-2 fuzzy sets in some cases. In this paper, a PSO type-reduction method for GIT-2FLS based on the particle swarm optimization （PSO） algorithm is presented. With the PSO type-reduction, the inference principle of geometric interval FLS operating on the continuous domain is consistent with that of traditional interval type-2 FLS operating on the discrete domain. With comparative experiments, it is proved that the PSO type-reduction exhibits good performance, and is a satisfactory complement for the theory of GIT-2FLS.

Clustering： from Clusters to Knowledge

Data analysis and automatic processing is often interpreted as knowledge acquisition. In many cases it is necessary to somehow classify data or find regularities in them. Results obtained in the search of regularities in intelligent data analyzing applications are mostly represented with the help of IF-THEN rules. With the help of these rules the following tasks are solved： prediction, classification, pattern recognition and others. Using different approaches---clustering algorithms, neural network methods, fuzzy rule processing methods--we can extract rules that in an understandable language characterize the data. This allows interpreting the data, finding relationships in the data and extracting new rules that characterize them. Knowledge acquisition in this paper is defined as the process of extracting knowledge from numerical data in the form of rules. Extraction of rules in this context is based on clustering methods K-means and fuzzy C-means. With the assistance of K-means, clustering algorithm rules are derived from trained neural networks. Fuzzy C-means is used in fuzzy rule based design method. Rule extraction methodology is demonstrated in the Fisher＇s Iris flower data set samples. The effectiveness of the extracted rules is evaluated. Clustering and rule extraction methodology can be widely used in evaluating and analyzing various economic and financial processes.

Different Criteria for the Optimal Number of Clusters and Selection of Variables with R

One of the most important problems of clustering is to define the number of classes. In fact, it is not easy to find an appropriate method to measure whether the cluster configuration is acceptable or not. In this paper we propose a possible and non-automatic solution considering different criteria of clustering and comparing their results. In this way robust structures of an analyzed dataset can be often caught （or established） and an optimal cluster configuration, which presents a meaningful association, may be defined. In particular, we also focus on the variables which may be used in cluster analysis. In fact, variables which contain little clustering information can cause misleading and not-robustness results. Therefore, three algorithms are employed in this study： K-means partitioning methods, Partitioning Around Medoids （PAM） and the Heuristic Identification of Noisy Variables （HINoV）. The results are compared with robust methods ones.

Continuous Clustering Trajectory Stream of Moving Objects

The clustering of trajectories over huge volumes of streaming data has been rec- ognized as critical for many modem applica- tions. In this work, we propose a continuous clustering of trajectories of moving objects over high speed data streams, which updates online trajectory clusters on basis of incremental line- segment clustering. The proposed clustering algorithm obtains trajectory clusters efficiently and stores all closed trajectory clusters in a bi- tree index with efficient search capability. Next, we present two query processing methods by utilising three proposed pruning strategies to fast handle two continuous spatio-temporal queries, threshold-based trajectory clustering queries and threshold-based trajectory outlier detections. Finally, the comprehensive experi- mental studies demonstrate that our algorithm achieves excellent effectiveness and high effi- ciency for continuous clustering on both syn- thetic and real streaming data, and the propo- sed query processing methods utilise average 90% less time than the naive query methods.

Single Image Super-Resolution by Clustered Sparse Representation and Adaptive Patch Aggregation

A Single Image Super-Resolution (SISR) reconstruction method that uses clustered sparse representation and adaptive patch aggregation is proposed. First, we randomly extract image patch pairs from the training images, and divide these patch pairs into different groups by K-means clustering. Then, we learn an over-complete sub-dictionary pair offline from corresponding group patch pairs. For a given low-resolution patch, we adaptively select one sub-dictionary to reconstruct the high resolution patch online. In addition, non-local self-similarity and steering kernel regression constraints are integrated into patch aggregation to improve the quality of the recovered images. Experiments show that the proposed method is able to realize state-of-the-art performance in terms of both objective evaluation and visual perception.

Clustering Approaches for Overhead Reduction over Coordinated Multiple Points Network-MIMO Downlink Systems

Owing to the potential for intercell cochannel interference mitigation and significant spectral efficiency improvement, coordinating transmission techniques by multiple radio access points have recently attracted a lot of attention. In this paper, the system structure and mathematical signal model based on clustered structure are presented for multipoint coordinating downlink transmission, the clustered supercell configurations with static/dynamic approaches are discussed, and then optimal precod- ing design is provided for an accepted level of scheduling complexity and reduced signaling over- head. Some simulation results are given to evaluate the performance of different cell-clustering approaches, and to show that a clustered supercell size of 7 is a reasonable choice for clustered coordination with the given transmit power and the reduced feedback.

弱智儿童识记训练的实验研究

本研究作者设计了三种识记训练（数字材料的识记训练、分类训练、无关联材料识记训练）。结果表明：１．本实验所采用的识记训练在提高弱智儿记忆能力方面很有成效，。２．三种识记训练对轻、中、重度三类弱智儿童有不同的影响：在数字组织水平方面，轻、中度弱智儿童训练后均有显著提高，而重度弱智儿童只能获得较简单的数字组织。三类弱智儿童在类群集水平上都有显著提高。只有轻度弱智儿童的主观组织识记能力能迁移，中、重度弱智儿童这方面能力的提高不显著。３．弱智儿童的记忆缺陷与智力缺陷有关，因此对他们进行识记能力培养的同时，也要把帮助他们克服知觉、思维。

Symptom clustering in chronic gastritis based on spectral clustering

OBJECTIVE: Apply spectral clustering to analyze the patterns of symptoms in patients with chronic gastritis(CG).METHODS: Based on 919 CG subjects, we applied mutual information feature selection to choose the positively correlated symptoms with each pattern.Then, we used the Shi and Malik spectral clustering algorithm to select the top 20 correlated symptoms.RESULTS: We ascertained the results of six patterns.There were three categories for the pattern of accumulation of damp heat in the spleen-stomach(0.00332). There were six categories for the pattern of dampness obstructing the spleen-stomach(0.02466). There were two categories for the pattern of spleen-stomach Qi deficiency(0.013 89).There were three categories for the pattern of spleen-stomach deficiency cold(0.009 15). There were five categories for the pattern of liver-Qistagnation(0.01910).There were four categories for the pattern of stagnant heat in the liver-stomach(0.00585).CONCLUSION: Most of the spectral clustering results of the symptoms of CG patterns were in accordance with clinical experience and Traditional Chinese Medicine theory. Most categories suggested the nature and/or location of the disease.

Self-organizing dual clustering considering spatial analysis and hybrid distance measures

Dual clustering performs object clustering in both spatial and non-spatial domains that cannot be dealt with well by traditional clustering methods.However,recent dual clustering research has often omitted spatial outliers,subjectively determined the weights of hybrid distance measures,and produced diverse clustering results.In this study,we first redefined the dual clustering problem and related concepts to highlight the clustering criteria.We then presented a self-organizing dual clustering algorithm (SDC) based on the self-organizing feature map and certain spatial analysis operations,including the Voronoi diagram and polygon aggregation and amalgamation.The algorithm employs a hybrid distance measure that combines geometric distance and non-spatial similarity,while the clustering spectrum analysis helps to determine the weight of non-spatial similarity in the measure.A case study was conducted on a spatial database of urban land price samples in Wuhan,China.SDC detected spatial outliers and clustered the points into spatially connective and attributively homogenous sub-groups.In particular,SDC revealed zonal areas that describe the actual distribution of land prices but were not demonstrated by other methods.SDC reduced the subjectivity in dual clustering.

SOME DYNAMICAL PROPERTIESOF QUADRATIC RATIONAL MAPS

This paper studies the dynamics of the analytic family z + 1/z + b alld describes the topologyof the parameter space, structural stability and J-stability. The mapping class group of almostall maps of the above family is determined.

Cluster Partition Function and Invariants of 3-Manifolds

The author reviews some recent developments in Chern-Simons theory on a hyperbolic 3-manifold M with complex gauge group G. The author focuses on the case of G = SL(N, C) and M being a knot complement: M = S^3\ K. The main result presented in this note is the cluster partition function, a computational tool that uses cluster algebra techniques to evaluate the Chern-Simons path integral for G = SL(N, C). He also reviews various applications and open questions regarding the cluster partition function and some of its relation with string theory.

Gear Fault Recognition and Diagnosis Based on Ant Colony Optimization Algorithm

introduce a new kind of swarm intelligence algorithm, the Ant Colony Optimization （ACO） algorithm. Propose a clustering analysis model based on ACO, apply the model to recognition and diagnosis of operation state for gearbox. Testing four kinds of gears and clustering some characteristic parameters of the gear vibration signal, the conclusion shows that this method can recognize running state with accuracy and all speed. It is a new method for fault recognition and diagnosis.