Hierarchical K-means Algorithm Research Articles

Analysis of health related event detection in big data for physical education training movement detection.

Physical education and training are essential ways to improve the physical quality of the nation, and China has incorporated "building a healthy China" and "fitness for all" into its national development strategy, integrating a strong sports nation into the Chinese dream. The study of digital recording and automated training in sports is of profound value. Motion capture technology can digitally record the training process in a digital physical education training system. At the same time, accurate modeling and calculation can analyze the training effects and give appropriate guidance and feedback. This study develops a new and improved hierarchical K-means algorithm by combining the known classification algorithm K-means with a hierarchical algorithm. The performance of the old and new algorithms are compared and then applied to physical education training data to produce clustering results and analysis to reduce the model, which is used to reduce the number of parameters in the model and improve the recognition speed. The experimental results demonstrate that the relevant models proposed in this study achieve an average accuracy of 91.27% and 92.26%, respectively, which is better than a single network model and can effectively use big data for health event detection. The empirical results show that the improved model algorithm outperforms the single network model and can detect health events using big data.

Finding hierarchy of clusters

In this work a novel hierarchical clustering technique is proposed which can be used to find a hierarchical structure of data items in a dataset. The emphasis is on maintaining consistency of the clusters at each level in the hierarchy as well as to have a natural number of clusters at each level. We propose a method by using Cluster Number Assisted k-Means (CNAK) to find the possible natural numbers of clusters for that dataset. Next, we find the association between clusters at different levels. We propose three criteria and use them to remove the insignificant levels to obtain a final hierarchical structure. This method can be used to find the number of clusters at each of the different levels, the clusters at those levels and their association with clusters at adjacent levels. Naïve Hierarchical k-means algorithm uses a pre-determined branching factor at all the levels. In most traditional agglomerative and divisive clustering, at each different level of the hierarchical structure, exactly one node is split into several nodes for the next lower level. Either this number is the same for each parent or decided independently. Clusters with multiple parents are not allowed in traditional hierarchical clustering. However, in reality a cluster may naturally have multiple parents. Experimentation have been carried out on multiple datasets to demonstrate the effectiveness of the proposed technique.

Determining elements used to measure knowledge management in higher education institutions' research divisions

The purpose of the current study is to identify the variables used to quantify knowledge in Barranquilla's higher education institutions' research divisions. The research used cluster analysis with the Hierarchical Kmeans algorithm and was descriptive in nature. Convenience sampling, not probability, was used for the sample. The directors and vice-rectors of the research departments filled out a questionnaire using Likert and ordinal scales as part of the survey's data gathering process. In terms of gauging knowledge management, the research's findings led to the definition of three different sorts of profiles for research groups at higher education institutions.

КЛАСТЕРНЫЙ АНАЛИЗ ДЛЯ ТИПОЛОГИЗАЦИИ ПИЩЕВЫХ ПРОДУКТОВ И БЛЮД

The article deals with basic concepts of cluster analysis and data clustering. The authors give brief information on the history of cluster analysis and its first applications. The article gives the classification of methods by the way of data processing and analysis in cluster analysis. The detailed description of the popular, non- hierarchical K-means algorithm is given. When developing databases, their structure should provide for the division of products into clusters based on various characteristics. It is necessary to consider the division into clusters based on other characteristics, such as allergenicity (whether the product contains an allergic component or not) or carbohydrate content (important for diabetics). The content of protein, potassium and phosphates should be taken into account when developing diets for those suffering from kidney diseases. The presence of specific amino acids - for metabolic diseases, etc. In this way, food composition data and product clustering across different categories allow nutritionists to create interchangeable lists of meals with portion sizes, or lists of permitted and prohibited food products in terms of various diseases. The authors give the clustering of the database fragment of chemical composition of food products on the example of cottage cheese products and confectionary by one of the signs – the content of carbohydrates – in the R software environment by k-means. Food clusters based on carbohydrate content are very important in shaping the diet for diabetics. A visual gradation of products into clusters is demonstrated in the form of a dendrogram showing the degree of proximity of individual clusters. The resulting dendrogram contains 5 clusters. Cluster 4 includes the largest number of products (170 items) with an average carbohydrate content of 1.8 g with a variation range from 0 to 7.1 g. Food products and dishes that fall into this cluster are the least dangerous for people with diabetes. Cluster 5 includes only 8 products with a distribution of carbohydrates within the cluster from 62.60 to 80.40 g. This category of food should be excluded when preparing a diet for people with diabetes.

RETRACTED: Pressure source of athletes in field based on improved hierarchical K-means algorithm

Nowadays, with the development of economy, sports industry attracts more and more people's attention, and the number and scale of sports events continue to expand. But the fierce competition, for athletes, is not only physical competition, psychological competition is particularly important. Psychological pressure is an important factor that affects athletes to play at a high level in the competition field. How to analyze the pressure source of athletes in the competition field is very important for athletes to overcome the competition pressure. Therefore, this paper designs a method based on the improved hierarchical K-Means clustering algorithm (KMCA) to analyze the pressure source of athletes in the competition field, so as to help athletes overcome the pressure on the competition field and promote their high-level performance. In this method, firstly, 182 athletes were investigated by questionnaire to obtain the data of related psychological pressure sources. Secondly, because KMCA is sensitive to the selection of initial class center, the performance of KMCA is directly related to the selection of initial class. Aiming at the problem of KMCA, this paper proposes an improved hierarchical KMCA. Finally, the improved hierarchical KMCA is applied to the clustering analysis of sports competition pressure source data. Through simulation analysis, compared with KMCA, the improved layered KMCA proposed in this paper has a good performance improvement. The improved hierarchical KMCA proposed in this paper can be applied to the analysis of the pressure source of athletes in the competition field, which can analyze the pressure of athletes and get the characteristics of the pressure of athletes, so as to help athletes overcome the pressure in the competition field and achieve the mental health of the competition field?

Analysis of athletes’ stadium stress source based on improved layered K-means algorithm

Most of the research on stressors is in the medical field, and there are few analysis of athletes’ stressors, so it can not provide reference for the analysis of athletes’ stressors. Based on this, this study combines machine learning algorithms to analyze the pressure source of athletes’ stadium. In terms of data collection, it is mainly obtained through questionnaire survey and interview form, and it is used as experimental data after passing the test. In order to improve the performance of the algorithm, this paper combines the known K-Means algorithm with the layering algorithm to form a new improved layered K-Means algorithm. At the same time, this paper analyzes the performance of the improved hierarchical K-Means algorithm through experimental comparison and compares the clustering results. In addition, the analysis system corresponding to the algorithm is constructed based on the actual situation, the algorithm is applied to practice, and the user preference model is constructed. Finally, this article helps athletes find stressors and find ways to reduce stressors through personalized recommendations. The research shows that the algorithm of this study is reliable and has certain practical effects and can provide theoretical reference for subsequent related research.

Data Clustering with Actuarial Applications

Data clustering refers to the process of dividing a set of objects into homogeneous groups or clusters such that the objects in each cluster are more similar to each other than to those of other clusters. As one of the most popular tools for exploratory data analysis, data clustering has been applied in many scientific areas. In this article, we give a review of the basics of data clustering, such as distance measures and cluster validity, and different types of clustering algorithms. We also demonstrate the applications of data clustering in insurance by using two scalable clustering algorithms, the truncated fuzzy c-means (TFCM) algorithm and the hierarchical k-means algorithm, to select representative variable annuity contracts, which are used to build predictive models. We found that the hierarchical k-means algorithm is efficient and produces high-quality representative variable annuity contracts.

Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations.

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area.

Content-Based Image Retrieval Using Color, Texture and Shape Features

Nowadays CBIR is getting more and more attention from organizations and researchers due to advances in digital imaging techniques. A lot of interest is getting paid to search images from large databases, as it is not only difficult and time-consuming task but sometimes frustrating for the users. This paper proposes the CBIR system based on color, texture and shape features. The proposed method employs the use of DCT and DWT along with Hierarchical k-means algorithm for faster retrieval of images. The efficiency of the given method is demonstrated by the results in the paper.

Part family formation for reconfigurable manufacturing system using K-means algorithm

The reconfigurable manufacturing systems (RMSs) is the next step in manufacturing, allowing the production of any quantity of highly customised and complex parts together with the benefits of mass production. In RMSs, parts are grouped into families, each of which requires a specific system configuration. Initially system is configured to produce the first family of parts. Once it is finished, the system is reconfigured in order to produce the second family, and so forth. The effectiveness of a RMS depends on the formation of the optimum set of part families addressing various reconfigurability issues. The aim of this work is to establish a methodology for grouping parts into families for effective working of reconfigurable manufacturing systems (RMSs). The methodology carried out in two phases. In the first phase, the correlation matrix is used as similarity coefficient matrix to form the part operational similarity followed by agglomerative hierarchical K-means algorithm used for the parts family formation. In the second phase, cost model is developed to select and sequence the formed part families, resulting in a minimum cost solution to the problem.

A novel approach for part family formation using K-means algorithm

The reconfigurable manufacturing system (RMS) is the next step in manufacturing, allowing the production of any quantity of highly customized and complex parts together with the benefits of mass production. In RMSs, parts are grouped into families, each of which requires a specific system configuration. Initially system is configured to produce the first family of parts. Once it is finished, the system will be reconfigured in order to produce the second family, and so forth. The effectiveness of a RMS depends on the formation of the optimum set of part families addressing various reconfigurability issues. The aim of this work is to establish a methodology for grouping parts into families for effective working of RMS. The methodology carried out in two phases. In the first phase, the correlation matrix is used as similarity coefficient matrix. In the second phase, agglomerative hierarchical K-means algorithm is used for the parts family formation resulting in an optimum set of part families for reconfigurable manufacturing system.

A novel approach for part family formation for reconfiguration manufacturing system

The Reconfigurable Manufacturing Systems (RMS) is the next step in manufacturing, allowing the production of any quantity of highly customised and complex parts together with the benefits of mass production. In RMSs, parts are grouped into families, each of which requires a specific system configuration. Initially system is configured to produce the first family of parts. Once it is finished, the system is reconfigured in order to produce the second family, and so forth. The effectiveness of a RMS depends on the formation of the optimum set of part families addressing various reconfigurability issues. For this, a two-phase approach is developed where parts are first grouped into families and then families are sequenced, computing the required machines and modules configuration for each family. In the First phase, parts are grouped into families based on their common features. The correlation matrix is developed as operations sequence similarity coefficient matrix. Principal Component Analysis (PCA) is applied to find the eigenvalues and eigenvectors on the correlation similarity matrix. A scatter plot analysis as a cluster analysis is applied to make parts groups while maximizing correlation between parts as per operations sequence similarity. Agglomerative Hierarchical K-means algorithm improved the parts family formation using Euclidean distance resulting a set of part families. In the second phase, optimal selection and sequences of the resulted part families is achieved by using a Mixed Integer Linear Programming (MILP) model minimizing reconfigurability and under-utilization costs to get the minimum cost solution.

Toward an Improved Clustering of Large Data Sets Using Maximum Common Substructures and Topological Fingerprints

A new clustering algorithm was developed that is able to group large data sets with more than 100,000 molecules according to their chemotypes. The algorithm preclusters a data set using a fingerprint version of the hierarchical k-means algorithm. Chemotypes are extracted from the terminal clusters via a maximum common substructure approach. Molecules forming a chemotype have to share a predefined number of rings, atoms, and non-carbon heavy atoms. In an iterative procedure, similar chemotypes and singletons are fused to larger chemotypes. Singletons that cannot be assigned to any chemotype are then grouped based on the proportion of overlap between the molecules. Representatives from each chemotype and the singletons are used in a second round of the hierarchical k-means algorithm to provide a final hierarchical grouping. Results are reported to an interactive graphical user interface which allows initial insights about the structure activity relationship (SAR) of the molecules. Example applications are shown for two chemotypes of reverse transcriptase inhibitors in the MDDR database and for the evaluation of descriptor-based similarity searching routines. A special focus was laid on the chemotype hopping potential of each individual routine. The algorithm will allow the analysis of high-throughput and virtual screening results with improved quality.