Similarity Clustering Algorithm Research Articles

Security Improvement of UAV Group IoT in Polygonal Area Based on Similarity Clustering Algorithm

Security Improvement of UAV Group IoT in Polygonal Area Based on Similarity Clustering Algorithm

Minimum cost consensus model with loss aversion based large-scale group decision making

In minimum cost consensus problems, to accomplish the group consensus, decision makers accommodate their initial discrepant opinions based on the unit adjustment costs. During this process, decision makers may exhibit loss aversion, which results in extra expenses when participants feel loss in contrast to their reference opinion adjustments. Existing minimum cost consensus models pay little attention to the loss-averse preference. Hence, to fill up this gap, a minimum cost consensus model with loss aversion (MCCM-LA) is established and the desired properties are analyzed. To manage large-scale group decision making problems, we first propose an integrated opinion similarity, connectivity similarity and behavior similarity clustering algorithm to divide decision makers into multiple subgroups. Balancing the individual adjustment willingness and consensus reaching efficiency, a two-stage consensus reaching mechanism is further designed based on MCCM-LA to realize the accordant opinion. Finally, the effectiveness and feasibility of the proposed method are demonstrated by sensitivity and comparative analyses with an illustrative example.

Consumer-Transformer Relationship Identification Based on Two-scale similarity and SC Algorithm

In view of the chaotic relationship between user-transformer in the current low-voltage stations, manual identification consumes a long time period, low identification accuracy, and high input system costs. This paper proposes a method for identifying household change relations in low-voltage stations based on dual-scale similarity and spectral clustering algorithm. First, according to the different similarities of user voltage curves in different stations, consider Euclidean distance and cosine distance to establish a two-scale spectral clustering similarity matrix from the distance and shape of the voltage curve. Then based on the two-scale similarity matrix, perform spectral clustering on the voltage curves of the same station area to complete the clustering of users in the same station area. Examples show that the method proposed in the article has high recognition accuracy, low input cost, and has good application effects.

Improved similarity coefficient and clustering algorithm for cell formation in cellular manufacturing systems

Cell formation plays an important role in the design of cellular manufacturing systems. Among many methods utilized to solve the cell formation problem, the similarity coefficient method is the most widely used owing to its high flexibility and low computational requirement. However, generalized similarity coefficients ignore material flow, which is closely related to operation sequence and repeated operations. Moreover, most similarity coefficient-based clustering algorithms focus on the number of inter-cell movements but disregard distinction of the movement effort. To overcome these limitations, this study improves the generalized similarity coefficient method to form part families. In addition, a new clustering algorithm is presented to assign machines to cells with minimum intensity of material inter-cell movement, which depends on the frequency, production volume and difficulty level of inter-cell movement. Experimental results demonstrate that the proposed method has superior sensitivity and effectiveness for solving the cell formation problem.

Semi-Supervised Patient Similarity Clustering Algorithm Based on Electronic Medical Records

Electronic medical record (EMR) is the comprehensive description of the patients' individual health information in medical activities, which paves the way for intelligent-assisted medical decision-making research. However, due to the problems of data preprocessing, time-consuming and laborious data labeling in the Chinese electronic medical records, and the diversity of electronic medical record data storage, the research on electronic medical records poses certain challenges. Therefore, this paper intends to construct a medical domain dictionary in the word segmentation to improve the accuracy of the Chinese medical terminology recognition in electronic medical records. The Chinese text feature is extracted by using the latent Dirichl et al location (LDA) model, and the patient feature vector is constructed by feature stitching. At the same time, this paper uses the cumulative method of multi-impact indicators to construct the patient paired constraint set as the supervision information and guides the cluster learning model to optimize the patient category effect. In this paper, the comparison results show that the clustering algorithm with supervisory information is better than the simple unsupervised clustering algorithm. When constructing the supervised information set, the influence factor of the multi-dimensional attribute of the patient is better than the single-diagnosis result factor, and the clustering effect is improved as the number of paired constraint information increases.

A hierarchical combinatorial testing method for smart phone software in wearable IoT systems

As the Internet of Things technology is finding more wide applications; wearable smart systems such as smart phones have gradually come into our lives. The smart wearable systems with numerous states usually lead to various unanticipated problems. A connective and semantic similarity clustering algorithm (CSSCA) and a hierarchical combinatorial test model based on finite state machine (FSM) are proposed to solve the problem. The FSM model of user manipulations is usually used to model the system design specification of a smart phone for black-box testing, and then it is converted into a regular expression, and some testing cases are generated according to the regular expression. Many experiments show that a large in scale and complicated in structure smart phone software can be tested using our innovative algorithms to discover more deep hidden logical errors efficiently and effectively.

基于耦合关系的医生用药异常分析

近些年来，医患关系受到广泛关注。如何准确地挖掘异常用药是制约医生和缓减医患关系的关键。本文提出了一种检测医生用药异常的总体框架。该框架集成处方数据的耦合相似度度量和变色龙聚类算法，并分为三个阶段，定性分析，定量分析和异常检测。在真实处方数据上进行了充分的实验，实验验证了该框架能够有效地检测出医生用药异常。 In recent years, the doctor-patient relationship has received extensive attention. How to exactly mine the abnormal use of drugs is the vital to constrain doctors and relieve the doctor-patient relationship. This paper presents a general framework for detecting the abnormal use of drugs. The framework integrates coupled similarity and Chameleon clustering algorithm, and consists of three stages, qualitative analysis, quantitative analysis and abnormal detection. We conduct extensive experiments on real-world prescription data. The experiments evaluate that the framework can efficiently detect abnormal use of drugs.

Relationship discovery in public opinion and actual behavior for social media stock data space

With the rise of social data media, the cyber world nearly parallels to the real world. The trajectory of a hot event is reflected in social media by Public Opinion Data Space (OS) and Actual Behavior Data Space (BS). However, the relationships with a variety of mechanisms in each space or between them are often unknown. To solve the above issues, the traditional methods for inferring relationship are by performing a statistical similarity analysis of time sequence from dynamic elements. In specially, the research of clustering nonlinear correlation data object is rare, so we propose Matrix Similarity Clustering Algorithm (MSCA) based on random matrix theory and combined with sliding window technology to cluster the similarity of multidimensional time sequences. This method is effective to detect the trend relationship of time sequences with multiple dynamic elements. In addition, we construct a knowledge map to analyse the relationships in OS and BS.

Real-time identification of pedestrian meeting and split events from surveillance videos using motion similarity and its applications

A real-time system to automatically identify pedestrian meeting events from surveillance videos is proposed. The system consists of three components: a pedestrian detection and tracking module, a pedestrian group identification module and a pedestrian group record. A three-level blob filter is used to improve the accuracy of pedestrian detection in the pedestrian detection and tracking module. Our previous work, the non-recursive motion similarity clustering algorithm is used as the pedestrian group identification module. Groups are detected within a time period of 0.02 ms (for four pedestrians in the scene) to 0.05 ms (for 32 pedestrians in the scene) of their occurrences in the video, using an Intel I7 processor-based machine. The pedestrian groups identified by this algorithm are stored in pedestrian group records, which are used subsequently to identify pedestrian meeting events. Visualizations were created to highlight the pedestrian groups, their history of group membership and the spatial distribution. With these visualizations, the enforcement agencies no longer need to browse through entire video archives for investigation purposes. We implemented the system to monitor several locations simultaneously in residential halls at the National University of Singapore. Our system was able to handle successfully 18 digital 640 $$\times$$ 480 pixel video streams at 25 fps on a moderately loaded Ethernet, monitoring a maximum of 30 pedestrians and detecting 83 % of the meeting and split events.

MODIS NDVI time series clustering under dynamic time warping

For MODIS NDVI time series with cloud noise and time distortion, we propose an effective time series clustering framework including similarity measure, prototype calculation, clustering algorithm and cloud noise handling. The core of this framework is dynamic time warping (DTW) distance and its corresponding averaging method, DTW barycenter averaging (DBA). We used 12 years of MODIS NDVI time series to perform annual land-cover clustering in Poyang Lake Wetlands. The experimental result shows that our method performs better than classic clustering based on ordinary Euclidean methods.

Adaptive Super-Resolution Image Reconstruction Algorithm of Neighborhood Embedding Based on Nonlocal Similarity

Super resolution technology origins from the field of image restoration. The increasing difficulties in the resolution improvement by hardware prompts the super resolution reconstruction that can solve this problem effectively, but the general algorithms of super resolution reconstruction model are unable to quickly complete the image processing. Based on this problem, this paper studies on adaptive super-resolution reconstruction algorithm of neighbor embedding based on nonlocal similarity, in the foundation of traditional neighborhood embedding super resolution reconstruction method, using nonlocal similarity clustering algorithm, classifying the image training sets, which reduces the matching search complexity and speeds up the algorithm; by introducing new characteristic quantity and building a new calculation formula for solving weights, the quality of reconstruction is enhanced. The simulation test shows that the algorithm proposed in this paper is superior to the traditional regularization method and the spline interpolation algorithm no matter on the objective index about statistic and structural features or subjective evaluation

Study on Fuzzy Clustering Algorithm of Spatial Data Mining

With rapid development of space information crawl technology, different types of spatial database and data size of spatial database increases continuously. How to extract valuable information from complicated spatial data has become an urgent issue. Spatial data mining provides a new thought for solving the problem. The paper introduces fuzzy clustering into spatial data clustering field, studies the method that fuzzy set theory is applied to spatial data mining, proposes spatial clustering algorithm based on fuzzy similar matrix, fuzzy similarity clustering algorithm. The algorithm not only can solve the disadvantage that fuzzy clustering cant process large data set, but also can give similarity measurement between objects.

A genetic clustering algorithm using a message-based similarity measure

In this paper, a genetic clustering algorithm is described that uses a new similarity measure based message passing between data points and the candidate centers described by the chromosome. In the new algorithm, a variable-length real-value chromosome representation and a set of problem-specific evolutionary operators are used. Therefore, the proposed GA with message-based similarity (GAMS) clustering algorithm is able to automatically evolve and find the optimal number of clusters as well as proper clusters of the data set. Effectiveness of GAMS clustering algorithm is demonstrated for both artificial and real-life data set. Experiment results demonstrated that the GAMS clustering algorithm has high performance, effectiveness and flexibility.

Structural similarity clustering for detection of outliers and hubs using minimum spanning tree

Clustering is a process of discovering groups of objects such that the objects of the same group are similar, and objects belonging to different groups are dissimilar. A number of clustering algorithms exist that can solve the problem of clustering. Minimum spanning tree based clustering algorithm is capable of detecting clusters with irregular boundaries. MST based Clustering is an important task for the discovery of underlying structures in graph. In data mining detection of anomalous pattern in data is more interesting than detecting inliers. Many algorithms find clusters by maximizing the number of intra-cluster edges. While such algorithms find useful and interesting structures, they tend to fail to identify and isolate two kinds of vertices that play special roles – vertices that bridge clusters (hubs) and vertices that are marginally connected to clusters (outliers). Identifying hubs is useful for applications such as viral marketing and epidemiology since hubs are responsible for spreading ideas and disease. In contrast, outliers have little or no influence, and many may be isolated as noise in the data. In this paper we propose a novel algorithm called Structural Similarity Clustering Algorithm using Minimum Spanning Tree (SSCAMST), which detect clusters, outliers and hubs in graph. The key feature of our algorithm is it finds noise-free/error-free clusters.

Conformational Analysis in a Multidimensional Energy Landscape: Study of an Arginylglutamate Repeat

The identification of the distinct conformation classes of a molecule is a common and often crucial step in establishing structure-function relationships. Many different methods have been suggested for that purpose which differ in their choice of a (dis)similarity measure and clustering algorithm. The present study discusses and analyzes these issues, proposing a method based on principal component analysis (PCA), which is applied to conformations obtained from molecular dynamics (MD) simulations of an arginylglutamate repeat. Simulations are done at different pH values, using both standard MD and constant-pH MD methods, with the peptide displaying a very high conformational variety. The conformational analysis starts with a comprehensive comparison of different sets of conformational coordinates and of their ability to preserve structural similarity between conformations. The selected set of conformational coordinates is then used to investigate the preservation of structural similarity after PCA transformation, concluding the need of using a multidimensional conformation space. This conformation space is then used to derive a multidimensional probability density and the corresponding energy landscape. The application of a simple cutoff algorithm to the resulting multidimensional landscape is then shown to produce a consistent set of distinct and homogeneous conformation classes. Overall, this methodology provides an efficient way to identify the major conformation classes of a molecule in a way that directly reflects the density of states in the multidimensional conformation space, contrasting with the more heuristic nature of standard clustering methods.

A purchase-based market segmentation methodology

Abstract Market segmentation is critical for a good marketing and customer relationship management program. Traditionally, a marketer segments a market using general variables such as customer demographics and lifestyle. However, several problems have been identified and make the segmentation result unreliable. This paper develops a novel market segmentation methodology based on product specific variables such as purchased items and the associative monetary expenses from the transactional history of customers to resolve these problems. A purchase-based similarity measure, clustering algorithm, and clustering quality function are defined in this paper. A genetic algorithm approach is adopted to ensure that customers in the same cluster have the closest purchase patterns. After completing segmentation, a designated RFM model is used to analyze the relative profitability of each customer cluster. The findings from a practical marketing implementation study will also be discussed.

Detecting stable clusters using principal component analysis.

Clustering is one of the most commonly used tools in the analysis of gene expression data (1, 2) . The usage in grouping genes is based on the premise that co-expression is a result of co-regulation. It is thus a preliminary step in extracting gene networks and inference of gene function (3, 4) . Clustering of experiments can be used to discover novel phenotypic aspects of cells and tissues (3, 5, 6) , including sensitivity to drugs (7) , and can also detect artifacts of experimental conditions (8) . Clustering and its applications in biology are presented in greater detail in the chapter by Zhao and Karypis (see also (9) ). While we focus on gene expression data in this chapter, the methodology presented here is applicable for other types of data as well. Clustering is a form of unsupervised learning, i.e. no information on the class variable is assumed, and the objective is to find the “natural” groups in the data. However, most clustering algorithms generate a clustering even if the data has no inherent cluster structure, so external validation tools are required. Given a set of partitions of the data into an increasing number of clusters (e.g. by a hierarchical clustering algorithm, or k-means), such a validation tool will tell the user the number of clusters in the data (if any). Many methods have been proposed in the literature to address this problem (10–15) . Recent studies have shown the advantages of sampling-based methods (12, 14) . These methods are based on the idea that when a partition has captured the structure in the data, this partition should be stable with respect to perturbation of the data. Bittner et al. (16) used a similar approach to validate clusters representing gene expression of melanoma patients. The emergence of cluster structure depends on several choices: data representation and normalization, the choice of a similarity measure and clustering algorithm. In this chapter we extend the stability-based validation of cluster structure, and propose stability as a figure of merit that is useful for comparing clustering solutions, thus helping in making these choices. We use this framework to demonstrate the ability of Principal Component Analysis (PCA) to extract features relevant to the cluster structure. We use stability as a tool for simultaneously choosing the number of principal components and the number of clusters; we compare the performance of different similarity measures and normalization schemes. The approach is demonstrated through a case study of yeast gene expression data from Eisen et al. (1) . For yeast, a functional classification of a large number of genes is known, and we use this classification for validating the results produced by clustering. A method for comparing clustering solutions specifically applicable to gene expression data was introduced in (17) . However, it cannot be used to choose the number of clusters, and is not directly applicable in choosing the number of principal components. The results of clustering are easily corrupted by the addition of noise: even a few

FLEXCLUS, AN INTERACTIVE PROGRAM FOR CLASSIFICATION AND TABULATION OF ECOLOGICAL DATA

SUMMARY A new computer program for the clustering of ecological data is presented. It is different from the programs which are commonly used in two aspects: Firstly, the program is interactive to a large extent, which implies that a priori decisions are limited. Secondly, the results of the clustering can be evaluated in terms of environmental data for the sites. The program provides several options to construct a species by site table. It is concluded that interactive clustering is preferable when data properties in terms of the (dis)similarity measure or clustering algorithm are less familiar to the user of the program.