Quantum Hierarchical Agglomerative Clustering Based on One Dimension Discrete Quantum Walk with Single-Point Phase Defects

As an important branch of machine learning,clustering analysis is widely used in some fields,e.g.,image pattern recognition,social network analysis,information security,and so on.In this paper,we consider the designing of clustering algorithm in quantum scenario,and propose a quantum hierarchical agglomerative clustering algorithm,which is based on one dimension discrete quantum walk with single-point phase defects.In the proposed algorithm,two nonclassical characters of this kind of quantum walk,localization and ballistic effects,are exploited.At first,each data point is viewed as a particle and performed this kind of quantum walk with a parameter,which is determined by its neighbors.After that,the particles are measured in a calculation basis.In terms of the measurement result,every attribute value of the corresponding data point is modified appropriately.In this way,each data point interacts with its neighbors and moves toward a certain center point.At last,this process is repeated several times until similar data points cluster together and form distinct classes.Simulation experiments on the synthetic and real world data demonstrate the effectiveness of the presented algorithm.Compared with some classical algorithms,the proposed algorithm achieves better clustering results.Moreover,combining quantum cluster assignment method,the presented algorithm can speed up the calculating velocity.

A weight's agglomerative method for detecting communities in weighted networks based on weight's similarity

This paper proposes the new definition of the community structure of the weighted networks that groups of nodes in which the edge＇s weights distribute uniformly but at random between them. It can describe the steady connections between nodes or some similarity between nodes＇ functions effectively. In order to detect the community structure efficiently, a threshold coefficient t~ to evaluate the equivalence of edges＇ weights and a new weighted modularity based on the weight＇s similarity are proposed. Then, constructing the weighted matrix and using the agglomerative mechanism, it presents a weight＇s agglomerative method based on optimizing the modularity to detect communities. For a network with n nodes, the algorithm can detect the community structure in time O（n2 log~）. Simulations on networks show that the algorithm has higher accuracy and precision than the existing techniques. Furthermore, with the change of t~ the algorithm discovers a special hierarchical organization which can describe the various steady connections between nodes in groups.

Regional Planning and Development Under the Maximization of Urban Agglomerative Economies

Starting from the meaning and types of urban agglomerative economies, with the analysis of the characteristicsand causes of urban agglomerative economies, this paper puts forward that the regional planning and development shouldattach importance to urban agglomerative economies, follow the law of the maximization of regional urban agglomerativeeconomies. It also points out the countermeasures and advices to facilitate the regional planning and development based on theprinciple.

An Efficient Agglomerative Clustering Algorithm for Web Navigation Pattern Identification

Web log mining is analysis of web log files with web page sequences. Discovering user access patterns from web access are necessary for building adaptive web servers, to improve e-commerce, to carry out cross-marketing, for web personalization, to predict web access sequence etc. In this paper, a new agglomerative clustering technique is proposed to identify users with similar interest, and to determine the motivation for visiting a website. Using this approach, web usage mining is done through different stages namely data cleaning, preprocessing, pattern discovery and pattern analysis. Results are given to explain how this approach produces tight usage clusters than the existing web usage mining techniques. Rather than traditional distance based clustering, the similarity measure is considered during clustering process in order to reduce computational complexity. This paper also deals with the problem of assessing the quality of user session clusters and cluster validity is measured by using statistical test, which measures the distances of clusters distributions to infer their dissimilarity and distinguish level. Using such statistical measures, it is proved that cluster accuracy is improved to the extent of 0.83, over existing k-means clustering with validity measure 0.26, FCM (Fuzzy C Means) clustering with validity measure 0.56. Rough set based clustering with validity measure 0.54 Generation of dense clusters is essential for finding interesting patterns needed for further mining and analysis.

基于多相参处理间隔频响特征聚类的有源假目标鉴别方法

现有真-假目标智能识别算法大多基于监督学习,且在低信噪比条件下表现不好。针对上述问题,该文分别利用真、假目标在多个相参处理间隔(CPIs)内散射特性的时变性和唯一性,提出一种多相参处理间隔频响特征聚类的真、假目标无监督鉴别方法。首先,在快-慢时域中沿快时间维度对真、假目标进行加窗截断,提取快-慢时间域频率响应特征用于构建初步样本集;然后,通过Agglomerative聚类和特征融合网络组成的两步识别算法对真-假目标进行识别;最后,提出一种多相参处理间隔联合决策方法提升识别性能和可靠性。经仿真和实测数据检验,证明了所提方法可实现真实目标和多种有源假目标的有效分离。

Analyzing Population Density Disparity in China with GIS-automated Regionalization: The Hu Line Revisited

The famous ’Hu Line’, proposed by Hu Huanyong in 1935, divided China into two regions(southeast and northwest) of comparable area size but drastically different in population. However, the classic Hu Line was derived manually in absence of reliable census data and computational technologies of modern days. It has been subject to criticism of lack of scientific rigor and accuracy. This research uses a GIS-automated regionalization method, termed REDCAP(Regionalization with Dynamically Constrained Agglomerative Clustering and Partitioning), to reconstruct the demarcation line based on the 2010 county-level census data in China. The results show that the logarithmic transformation of population density is a better measure of attributive homogeneity in derived regions than density itself, and produces two regions of nearly identical area size and greater contrast in population. Specifically, the revised Hu Line by Hu Huanyong in 1990 had the southeast region with 94.4% of total population and 42.9% of total land, and our delineation line yields a southeast region with 97.4% population and 50.8% land. Therefore, the population density ratio of the two regions is 27.1 by our line, much higher than the ratio of 22.4 by the Hu Line, and thus outperforms the Hu Line in deriving regions of maximum density contrast with comparable area size. Furthermore, more regions are delineated to further advance our understanding of population distribution disparity in China.

Chinese multi-document personal name disambiguation

This paper presents a new approach to determining whether an interested personal name across doeuments refers to the same entity. Firstly,three vectors for each text are formed： the personal name Boolean vectors denoting whether a personal name occurs the text the biographical word Boolean vector representing title, occupation and so forth, and the feature vector with real values. Then, by combining a heuristic strategy based on Boolean vectors with an agglomeratie clustering algorithm based on feature vectors, it seeks to resolve multi-document personal name coreference. Experimental results show that this approach achieves a good performance by testing on ＂Wang Gang＂ corpus.

Simulation research on monomer agglomeration of nonmetallic inclusions in steel with a diffusion limited aggregation model

The monomer agglomeration of nonmetallic inclusions was simulated with a diffusion limited aggregation （DLA） model of the fractal theory. The simulation study with a random two-dimensional diffusion was carried out. The results indicate that the DLA model can be used for the simulation of agglomeration behavior of the cluster-type inclusions. The morphology of clusters was observed with SEM and compared with the simulated agglomerates. The modelling procedure of the DLA model is applicable for the agglomeration process. The uncertainty of agglomeration process and the persuasive average agglomerative ratio was analyzed. The factors about the agglomerative ratio with the collision path distance and the size of particles or seed were discussed. The adherence of the nonmetallic inclusions on the dam, the weir and the walls of a tundish, and the absorption of inclusions by stopper or nozzle were also discussed.

Safest Route Detection via Danger Index Calculation and K-Means Clustering

The study aims to formulate a solution for identifying the safest route between any two inputted Geographical locations.Using the New York City dataset,which provides us with location tagged crime statistics;we are implementing different clustering algorithms and analysed the results comparatively to discover the best-suited one.The results unveil the fact that the K-Means algorithm best suits for our needs and delivered the best results.Moreover,a comparative analysis has been performed among various clustering techniques to obtain best results.we compared all the achieved results and using the conclusions we have developed a user-friendly application to provide safe route to users.The successful implementation would hopefully aid us to curb the ever-increasing crime rates;as it aims to provide the user with a beforehand knowledge of the route they are about to take.A warning that the path is marked high on danger index would convey the basic hint for the user to decide which path to prefer.Thus,addressing a social problem which needs to be eradicated from our modern era.

A Progressive Approach to Generic Object Detection: A Two-Stage Framework for Image Recognition

Object detection in images has been identified as a critical area of research in computer vision image processing.Research has developed several novel methods for determining an object’s location and category from an image.However,there is still room for improvement in terms of detection effi-ciency.This study aims to develop a technique for detecting objects in images.To enhance overall detection performance,we considered object detection a two-fold problem,including localization and classification.The proposed method generates class-independent,high-quality,and precise proposals using an agglomerative clustering technique.We then combine these proposals with the relevant input image to train our network on convolutional features.Next,a network refinement module decreases the quantity of generated proposals to produce fewer high-quality candidate proposals.Finally,revised candidate proposals are sent into the network’s detection process to determine the object type.The algorithm’s performance is evaluated using publicly available the PASCAL Visual Object Classes Challenge 2007(VOC2007),VOC2012,and Microsoft Common Objects in Context(MS-COCO)datasets.Using only 100 proposals per image at intersection over union((IoU)=0.5 and 0.7),the proposed method attains Detection Recall(DR)rates of(93.17%and 79.35%)and(69.4%and 58.35%),and Mean Average Best Overlap(MABO)values of(79.25%and 62.65%),for the VOC2007 and MS-COCO datasets,respectively.Besides,it achieves a Mean Average Precision(mAP)of(84.7%and 81.5%)on both VOC datasets.The experiment findings reveal that our method exceeds previous approaches in terms of overall detection performance,proving its effectiveness.

Composition,Structure and Regeneration Dynamics of Olea ferruginea Royle Forests from Hindukush Range of Pakistan

Abstract＂ The abundance of Olea ferruginea in Malakand Division has been significantly reduced across its distribution range due to anthropogenic pressure in the recent past. A number of initiatives were taken for grafting this species to obtain better seeds for oil production, without the basic information on their ecology and management. To address this knowledge gap, we quantified the composition, structure and regeneration dynamics of Olea ferruginea forests in Malakand Division, Hindukush range of Pakistan. In the present study, five communities dominated by Oleaferruginea were identified using Ward＇s agglomerative cluster analysis. Total tree density ranged from 153-26o2 plants/ha, and basal area from 19.55 to 2353 m~ ha-1 with Olea having a relative density of 51% to 87% and basal area of 48% to 93%, respectively. The density of juveniles of the dominant and subordinate tree species were generally low which reflect their narrow distribution in the study area. Size-class distributions of O. ferruginea disclosed a bell-shaped pattern, indicating that forests were heavily exploited by local inhabitants in previous periods and recently by armed forces owing to security risks in the study area. The age （mean max. 300±34 years） and annual increment （3.2±1.2 years/cm） indicates that the species is long lived and generally slow growing among the different broad leaved species studied so far. However, the oldest trees can be found by the exploration of large diameter trees in the area. In addition, we found a stable linear relationship between the age and diameter （r2 = o.779）, indicating that diameter is a good predictor of age for this broad leaved species. In view of its relatively slow growth, longevity and positive ring-width characteristics O. ferruginea seems to be a suitable choice for dendroecological and dendrochronological studies in lesser Himalayan and Hindukush ranges of Pakistan. The results obtained from this study may help in understanding the composition, structure and regeneration dynamics of other subtropical broad leaved species.

IAGNES algorithm for protocol recognition

In the process of protected protocol recognition,an improved AGglomerative NESting algorithm( IAGNES) with high adaptability is proposed,which is based on the AGglomerative NESting algorithm( AGNES),for the challenging issue of how to obtain single protocol data frames from multiprotocol data frames. It can improve accuracy and efficiency by similarity between bit-stream data frames and clusters,extract clusters in the process of clustering. Every cluster obtained contains similarity evaluation index which is helpful to evaluation. More importantly,IAGNES algorithm can automatically recognize the number of cluster. Experiments on the data set published by Lincoln Laboratory shows that the algorithm can cluster the protocol data frames with high accuracy.

A heuristic clustering algorithm based on high density-connected partitions

Clustering data with varying densities and complicated structures is important,while many existing clustering algorithms face difficulties for this problem. The reason is that varying densities and complicated structure make single algorithms perform badly for different parts of data. More intensive parts are assumed to have more information probably,an algorithm clustering from high density part is proposed,which begins from a tiny distance to find the highest density-connected partition and form corresponding super cores,then distance is iteratively increased by a global heuristic method to cluster parts with different densities. Mean of silhouette coefficient indicates the cluster performance. Denoising function is implemented to eliminate influence of noise and outliers. Many challenging experiments indicate that the algorithm has good performance on data with widely varying densities and extremely complex structures. It decides the optimal number of clusters automatically.Background knowledge is not needed and parameters tuning is easy. It is robust against noise and outliers.

Data Driven Customer Segmentation for Vietnamese SMEs in Big Data Era

Almost Vietnamese big businesses often use outsourcing services to do marketing researches such as analysing and evaluating consumer intention and behaviour,customers’satisfaction,customers’loyalty,market share,market segmentation and some similar marketing studies.One of the most favourite marketing research business in Vietnam is ACNielsen and Vietnam big businesses usually plan and adjust marketing activities based on ACNielsen’s report.Belong to the limitation of budget,Vietnamese small and medium enterprises(SMEs)often do marketing researches by themselves.Among the marketing researches activities in SMEs,customer segmentation is conducted by tools such as Excel,Facebook analytics or only by simple design thinking approach to help save costs.However,these tools are no longer suitable for the age of data information explosion today.This article uses case analysing of the United Kingdom online retailer through clustering algorithm on R package.The result proves clustering method’s superiority in customer segmentation compared to the traditional method(SPSS,Excel,Facebook analytics,design thinking)which Vietnamese SMEs are using.More important,this article helps Vietnamese SMEs understand and apply clustering algorithm on R in customer segmenting on their given data set efficiently.On that basis,Vietnamese SMEs can plan marketing programs and drive their actions as contextualizing and/or personalizing their message to their customers suitably.

Site Fidelity and Residency of Tursiops truncatus off the Aragua Coast,Venezuela-First Records of Long Residency

This study represents the first comprehensive analysis of the residency patterns of a coastal population of bottlenose dolphin off the coast of Aragua,Venezuela,over a multi-year period.Using photo-identification,the most recent study(2019-2020)identified 56 individuals with the time between encounters from one to 344 days between the first and last sighting.Site Fidelity(SF)and Residence(RES)indices were calculated and Agglomerative Hierarchical Clustering(AHC)modeling was performed,with three patterns of residence obtained:resident(25%),semi-resident(17.86%)and transient(57.14%).These results were contrasted with remodeled data from a previous study(2006-2007),showing similar patterns:resident(24.44%),semi-resident(28.89%)and transient(46.67%).Importantly,two individuals were found to have been resident over the extended period.A breeding female sighted for the first time in 2004 and again in 2020(16 years)and the other from 2005 to 2020(15 years).This region is an important area for marine mammals,known to support a resident reproductive population over many years,as well seabirds,sea turtles,whale sharks and fishermen.We recommend that consideration be given to designating the waters as a Marine Protected Area to safeguard the existing population and provide benefit to the surrounding marine environment.

Preparation of ultrafine α-Al_2O_3 powders by catalytic sintering of ammonium aluminum carbonate hydroxide at low temperature

The precursor of ammonium aluminum carbonate hydroxide was synthesized by using aluminum sulfate（Al2（SO4）3） and ammonium carbonate（（NH4）2CO3）. The effects of α-Al2O3 seeds and mixture composed of α-Al2O3 and ammonium nitrate, as well as multiplex catalysts （AT） on phase transformation of alumina in sintering process were investigated respectively. The results show that the α-Al2O3 seeds and the mixture of α-Al2O3 and ammonium nitrate can lower the phase transformation temperature of α-Al2O3 to different extents while the particles obtained agglomerate heavily. AT has great potential synergistic effects on the phase transformation of alumina and reduces the phase transformation temperature of α-Al2O3 and the trends of necking-formation between particles. Therefore the dispersion of powder particles is improved significantly.