Using Greedy algorithm: DBSCAN revisited II

INTRODUCTION Clustering groups dataset data into meaning-ful subclasses in such a way that minimizes theintra-differences and maximizes the in-ter-differences of these subclasses; and is one ofthe most widely studied problems in data mining.There are many application areas for clusteringtechniques, such as statistical data analysis, patternrecognition, image processing, and other businessprocesses etc. Many clustering algorithms havebeen proposed, in part…     

Using Greedy algorithm:DBSCAN revisited Ⅱ

The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clustering of Applications with Noise(DBSCAN)(Ester et al.,1996),and has the following advantages: first,Greedy algorithm substitutes for R*-tree(Bechmann et al.,1990)in DBSCAN to index the clustering space so that the clustering time cost is decreased to great extent and I/O memory load is reduced as well; second,the merging condition to approach to arbitrary-shaped clusters is designed carefully so that a single threshold can distinguish correctly all clusters in a large spatial dataset though some density-skewed clusters live in it. Finally,authors investigate a robotic navigation and test two artificial datasets by the proposed algorithm to verify its effectiveness and efficiency.     

A statistical information-based clustering approach in distance space

Clustering, as a powerful data mining technique for discovering interesting data distributions and patterns in the nderlying database, is used in many fields, such as statistical data analysis, pattern recognition, image processing, and other usiness applications. Density-based Spatial Clustering of Applications with Noise (DBSCAN) (Ester et al., 1996) is a good erformance clustering method for dealing with spatial data although it leaves many problems to be solved. For example, BSCA…     

A statistical information-based clustering approach in distance space

Clustering, as a powerful data mining technique for discovering interesting data distributions and patterns in the underlying database, is used in many fields, such as statistical data analysis, pattern recognition, image processing, and other business applications. Density-based Spatial Clustering of Applications with Noise (DBSCAN) (Ester et al., 1996) is a good performance clustering method for dealing with spatial data although it leaves many problems to be solved. For example,DBSCAN requires a necessary user-specified threshold while its computation is extremely time-consuming by current method such as OPTICS, etc. (Ankerst et al., 1999), and the performance of DBSCAN under different norms has yet to be examined. In this paper, we first developed a method based on statistical information of distance space in database to determine the necessary threshold. Then our examination of the DBSCAN performance under different norms showed that there was determinable relation between them. Finally, we used two artificial databases to verify the effectiveness and efficiency of the proposed methods.     

Document Clustering Based on Constructing Density Tree

This paper focuses on document clustering by clustering algorithm based on a DEnsityTree （CABDET） to improve the accuracy of clustering. The CABDET method constructs a density-based treestructure for every potential cluster by dynamically adjusting the radius of neighborhood according to local density. It avoids density-based spatial clustering of applications with noise （DBSCAN） ＇s global density parameters and reduces input parameters to one. The results of experiment on real document show that CABDET achieves better accuracy of clustering than DBSCAN method. The CABDET algorithm obtains the max F-measure value 0.347 with the root node＇s radius of neighborhood 0.80, which is higher than 0.332 of DBSCAN with the radius of neighborhood 0.65 and the minimum number of objects 6.     

Reordering and Reflecting Factors for Simulation Studies With Exploratory Factor Analysis

This article describes the REREFACT R package, which provides a postrotation algorithm that reorders or reflects factors for each replication of a simulation study with exploratory factor analysis (EFA). The purpose of REREFACT is to provide a general algorithm written in freely available software, R, dedicated to addressing the possibility that a nonuniform order or sign pattern of the factors could be observed across replications. The algorithm implemented in REREFACT proceeds in 4 steps. Step 1 determines the total number of equivalent forms, I, of the vector of factors, η. Step 2 indexes, i = 1, 2 … I, each equivalent form of η (i.e., ηⁱ) via a unique permutation matrix, P (i.e., Pⁱ). Step 3 determines which ηⁱ each replication follows. Step 4 uses the appropriate Pⁱ to reorder or re-sign parameter estimates within each replication so that all replications uniformly follow the order and sign pattern defined by the population values. Results from two simulation studies provided evidence for the efficacy of the REREFACT to identify and remediate equivalent forms of η in models with EFA only (i.e., Example 1) and in fuller parameterizations of exploratory structural equation modeling (i.e., Example 2). How to use REREFACT is briefly demonstrated prior to the Discussion section by providing annotations for key commands and condensed output using a subset of simulated data from Example 1.     

基于密度的聚类算法研究

聚类算法是数据挖掘的核心技术,基于密度的聚类是一类已经被证明非常有效的聚类方法．与DBSCAN算法作比较,文章提出了一种基于密度的聚类算法（Clustering Using Centers and Density,CUCD）．该算法是基于中心点以及密度实现的,其核心对象是根据数据分布计算出来的虚拟的点,并且核心对象的代表性随程序的执行次数而提高;经实验验证,该算法具有较好的时间效率和聚类质量．     

基于改进DBSCAN算法的异常数据处理

随着智慧农业的发展,农业生产中海量数据不断涌现。在海量数据中难免存在噪声数据,这些数据不仅难以提供有效价值,还会影响信息挖掘。针对该问题,采用基于密度的DBSCAN聚类算法进行异常数据处理。鉴于DBSCAN算法对参数敏感,结合数据集本身特性与统计学思想以绘制各点之间的距离升序曲线,预估出DBSCAN的Eps参数。仿真实验结果表明,改进算法平均准确率达到99.6%,较传统算法提高了1.7个百分点,并且在10次检测中,改进算法只有3个数据判定错误,证明该参数设置方法对异常数据处理准确率更高,稳定性也更好。     

雾沫夹带情况下蒸馏塔盘Murphree 效率预测新方法

A new empirical correlation has been presented for the effect of entrainment on distillation tray efficiency based on the results of numerical solution given by Lockett,et al.The calculated results are in good agreement with those of the numerical solution given by Lockett,et al.The average deviation is 1.14% and the maximum deviation is 4.76% for the ranges of 0相似文献   

Application of biomonitoring and support vector machine in water quality assessment

The behavior of schools of zebrafish (Danio rerio) was studied in acute toxicity environments. Behavioral features were extracted and a method for water quality assessment using support vector machine (SVM) was developed. The behavioral parameters of fish were recorded and analyzed during one hour in an environment of a 24-h half-lethal concentration (LC₅₀) of a pollutant. The data were used to develop a method to evaluate water quality, so as to give an early indication of toxicity. Four kinds of metal ions (Cu²⁺, Hg²⁺, Cr⁶⁺, and Cd²⁺) were used for toxicity testing. To enhance the efficiency and accuracy of assessment, a method combining SVM and a genetic algorithm (GA) was used. The results showed that the average prediction accuracy of the method was over 80% and the time cost was acceptable. The method gave satisfactory results for a variety of metal pollutants, demonstrating that this is an effective approach to the classification of water quality.     

一种改进的k-means聚类算法

针对k-means算法事先必须获知聚类数目以及难以确定初始中心的缺点,提出了一种改进的k-means聚类算法.首先引入轮廓系数的概念,通过计算不同K值下簇集中各对象的轮廓系数确定事先未知分类信息的数据集中所包含的最优聚类数Kopt;然后通过凝聚层次聚类的方法获得数据集的分布,确定初始聚类中心;最后利用传统的k-means方法完成聚类.理论分析表明,所提出的算法具有适度的计算复杂度.IRIS测试数据集的实验结果表明了该算法能够合理区分不同类型的簇集,且可以有效地识别离群点,聚合后的结果簇集具有较低的熵值.     

Increasing school personnel's self-efficacy,knowledge, and response regarding nonsuicidal self-injury in youth

Rates of students engaging in nonsuicidal self-injury (NSSI) are rising and additional supports in the schools are needed (Nock, 2010, Ann Rev Clin Psychol, 6, 339–363; Stargell et al., 2017, Prof Sch Couns, 21, 37-46). School psychologists, school counselors, and school nurses are key personnel in responding to self-injurious behaviors within the school setting. The results of a practice-based research project are described, in which school psychologists, school counselors, and school nurses participated in training to increase their self-efficacy, knowledge, and response in regard to NSSI. The training provided information regarding best practice in responding to NSSI in youth (Hasking et al., 2016, Sch Psychol Int, 37(6), 644–663; Kanan et al., 2008, Sch Psychol Forum: Res Prac, 2, 67–79; Walsh & Muehlenkamp, 2013, Sch Psychol Forum: Res Prac, 7, 161–171). This exploratory study indicated that training positively impacted participants' perceived self-efficacy and knowledge with respect to responding to youth who engage in NSSI. Handouts and resources for school-based staff are included. Limitations and future directions are discussed.     

An introduction to the special issue on wisdom and moral education

ABSTRACT

This essay introduces the present special issue on wisdom and moral education, which draws on a conference held in Oxford in 2017. Some of the seven contributions (by Sanderse; Ferkany; and Hatchimonji et al.) make use of the Aristotelian concept of phronesis, or practical wisdom, while others focus more on the wisdom concept as it has developed in contemporary psychology (Huynh and Grossman; Ardelt; and Brocato, Hix and Jayawickreme). One (by Swartwood) straddles the distinction between the two. All the contributions, however, address in different ways practical questions about how wisdom can be evaluated and how it relates to issues of moral development and education.     

一种优化初始聚类中心的自适应聚类算法

K 均值算法（K-Means）是聚类算法中最受欢迎且最健壮的一种算法,然而在实际应用中,存在真实数据集划分的类数无法提前确定及初始聚类中心点随机选择易使聚类结果陷入局部最优解的问题。因此提出一种基于最大距离中位数及误差平方和（SSE）的自适应改进算法。该算法根据计算获取初始聚类中心点,并通过 SSE 变化趋势决定终止聚类或继续簇的分裂,从而自动确定划分的类簇个数。采用 UCI 的 4 种数据集进行实验。结果表明,改进后的算法相比传统聚类算法在不增加迭代次数的情况下,聚类准确率分别提高了17.133%、22.416%、1.545%、0.238%,且聚类结果更加稳定。     

Alternative conceptions in Galilean relativity: inertial and non‐inertial observers

This concluding part of a study on Galilean relativity focuses on students’ notions with regard to the inertial and non‐inertial character of frames of reference. (See Panse et al. 1994, Ramadas et al. 1996). The results show that students: adopt kinematic criteria for deciding the inertial or non‐inertial character of frames; consider this character to be a ‘relative’ property of two frames rather than an intrinsic property of a given frame; and equate pseudo‐forces to ‘imaginary’ forces. Centrifugal force is associated with rotating objects rather than with rotating frames; the latter are localized by the finite extension of their associated objects. Anthropomorphic criteria are invoked to judge the existence of centrifugal force, which is regarded as a reaction (in the sense of Newton's third law) to the centripetal force on a rotating object.     

Random Permutation Testing Applied to Measurement Invariance Testing with Ordered-Categorical Indicators

We describe and evaluate a random permutation test of measurement invariance with ordered-categorical data. To calculate a p-value for the observed (?)χ², an empirical reference distribution is built by repeatedly shuffling the grouping variable, then saving the χ² from a configural model, or the ?χ² between configural and scalar-invariance models, fitted to each permuted dataset. The current gold standard in this context is a robust mean- and variance-adjusted ?χ² test proposed by Satorra (2000), which yields inflated Type I errors, particularly when thresholds are asymmetric, unless samples sizes are quite large (Bandalos, 2014; Sass et al., 2014). In a Monte Carlo simulation, we compare permutation to three implementations of Satorra’s robust χ² across a variety of conditions evaluating configural and scalar invariance. Results suggest permutation can better control Type I error rates while providing comparable power under conditions that the standard robust test yields inflated errors.     

一种K-means算法的k值优化方案

聚类算法是数据挖掘中核心技术之一,而k-means算法在经典聚类算法中占有重要地位。针对k-means聚类算法的最佳聚类个数k不易获得,因而使得该聚类算法的应用受到限制,为此提出一种k值优化方法:通过给出大于最佳聚类数的可能聚类数,而得到优化的聚类个数。通过实例给予验证,其结果说明该方法合理有效。     

Children's ideas about ecology 1: theoretical background,design and methodology

Abstract

This paper provides an introduction to a study of the ecological understandings of children aged 5‐16 years in schools in the north of England. Children's ideas about selected ecological concepts were elicited through a series of written tasks and individual interviews set in a range of contexts, referred to here as probes. Responses of about 200 pupils, across the age range, were obtained on each probe. In this paper, issues relating to theoretical background, design and methodology are outlined. Two further papers present the major findings of the study: the first reports children's ideas about the cycling of matter between organisms and between organisms and the abiotic environment (Leach et al. in press a); the second reports children's ideas about the interdependency of organisms in ecosystems (Leach et al. in press b).     

A class of Marcinkiewicz integral operators on product domains

In this paper, the author proves that theL ^p-boundedness of the Marcinkiewicz integral μ_Ω on product domainsR ⁿ×R^m; for Ω∈(1)∩(5) improves the result of Chen et al. (2000). Project supported by Major Project of NSFC (No.19631080) and NSF of Zhejiang province (No. RC97017).