An ANN-based Clustering Analysis Algorithm with Dynamic Data Window

This paper built a optimization model and proposed an improved firefly optimization algorithm called CFA, which is based on firefly Cooperative.The main idea of CFA is to extend the single population FA to the interacting multi-swarms by cooperative Models.In this work, firstly, CFA algorithm is used for optimizing six widely-used benchmark functions and the comparative results produced by, firefly optimization algorithm(FA) are studied.Secondly, CFA algorithm used in data mining, clustering analysis on several typical data sets.The performance of typical data clustering results showed that the biological heuristic algorithm based on clustering analysis algorithm with the existing success of FA compared to faster convergence, and the clustering of higher quality.

The data of GIS have spatial distribution. Geographic data has both spatial characteristics and attribute characteristics, and also changes with time. Therefore, the amount of data is very large. Nowadays, many industries and departments in the society are using GIS. However, without proper data analysis and mining scheme, GIS will not exert its maximum effectiveness and will waste a lot of data. In this paper, we use the geographic information demand of a national security department as the experimental object, combining the characteristics of GIS data, taking into account the characteristics of time, space, attributes and so on, and using cluster analysis algorithm. We further study the mining scheme for depth data, and get the algorithm model. This algorithm can automatically classify sample data, and then carry out exploratory analysis. The research shows that the algorithm model and the information mining scheme can quickly find hidden depth information from the surface data of GIS, thus improving the efficiency of the security department. This algorithm can also be extended to other fields.

In view of the advantages of deep learning model in the extraction of abstract concept, a new text clustering algorithm is designed based on Deep Boltzmann Machines. Based on Replicate Softmax Model and new Deep Boltzmann Machine, energy function of this model is proposed and the detail learning algorithm is introduced.

Based on multiple ethnic elements, we investigate the effectiveness of environmental art design by detailedly analyzing the mixed attribute information with real value, interval number or natural language. Considering the traditional numerical information(e.g., FCM clustering algorithm), we propose a new algorithm for clustering analysis. In this method, we first introduce the clustering analysis problem with hybrid multi index information, and put forward two theorems about determining the optimal partition and the optimal clustering center based on the hybrid multi index information, we then give the iteration steps of the FCM clustering algorithm for mixed attribute information. Based on the two optimal parameters, a case study of multi-ethnic elements is made, and the effectiveness of environmental art design is analyzed. Due to a lack of adequate characterization of single similarity measure, we employ a combined similarity clustering analysis method in this paper, i.e., make use of the combined similarity to summarize the relationship between various ethnic factors and environmental design, which greatly improves the reliability and comprehensiveness of similarity measure and, can be applying in making reasonable classification of the hybrid multi index information. This method, which is intuitive, easy to operate, and convenient for application, not only can be used in the study on environmental art design of ethnic elements, but also lays a foundation for further analysis of related issues.

Data mining is a nontrivial process so that we can identify the effective, unknown, potentially useful and ultimately apprehensible pattern from databases. Clustering analysis is an important approach of data mining. This paper introduces a new concept of Dynamic Data Windows, and then puts forward a new fusion clustering algorithm with Dynamic Data Windows, the idea of k-means algorithm and density-based method. This new fusion clustering algorithm overcomes some disadvantages of traditional methods. Comparing with clustering based on density, integrated clustering analysis algorithm and clustering based on ANN, the new fusion clustering algorithm is more valuable in data mining. This new fusion clustering algorithm was used in Geographic Information System (GIS). Some analysis results show that the significant improvement to ship-routing design using the new fusion clustering algorithm with Dynamic Data Windows in database of GIS.

The NORMIX data analysis program, which incorporates cluster analysis and multivariate statistical analysis routines, was modified and revised for use In a UNI VAC 1110 computer. The revised program was tested on three sample data sets and produced results in agreement with those from the original program. The NORMIX program was then used to evaluate and analyze eight sets of aerometric data from various sources. Comparison of the performance of NORMIX with two other cluster analysis algorithms, MIKCA and SAS CLUSTER, revealed that all three programs produce similar results In terms of hierarchical clustering, but NORMIX produces considerably more statistical evaluation and Information to the user. Thus NORMIX is recommended as the most useful cluster analysis program of these three.

Knowing a patient’s risk at the moment of admission to a medical unit is important for both clinical and administrative decision making: it is fundamental to carry out a health technology assessment. In this paper, we propose a non-supervised learning method based on cluster analysis and genetic algorithms to classify patients according to their admission risk. This proposal includes an innovative way to incorporate the information contained in the diagnostic hypotheses into the classification system. To assess this method, we used retrospective data of 294 patients (50 dead) admitted to two Adult Intensive Care Units (ICU) in the city of Santiago, Chile. An area calculation under the ROC curve was used to verify the accuracy of this classification. The results show that, with the proposed methodology, it is possible to obtain an ROC curve with a 0.946 area, whereas with the APACHE II system it is possible to obtain only a 0.786 area.

Bone consists of an organic and an inorganic matrix. During development, bone undergoes changes in its composition and structure. In this study we apply three different cluster analysis algorithms [K-means (KM), fuzzy C-means (FCM) and hierarchical clustering (HCA)], and discriminant analysis (DA) on infrared spectroscopic data from developing cortical bone with the aim of comparing their ability to correctly classify the samples into different age groups. Cortical bone samples from the mid-diaphysis of the humerus of New Zealand white rabbits from three different maturation stages (newborn (NB), immature (11 days-1 month old), mature (3-6 months old)) were used. Three clusters were obtained by KM, FCM and HCA methods on different spectral regions (amide I, phosphate and carbonate). The newborn samples were well separated (71-100% correct classifications) from the other age groups by all bone components. The mature samples (3-6 months old) were well separated (100%) from those of other age groups by the carbonate spectral region, while by the phosphate and amide I regions some samples were assigned to another group (43-71% correct classifications). The greatest variance in the results for all algorithms was observed in the amide I region. In general, FCM clustering performed better than the other methods, and the overall error was lower. The discriminate analysis results showed that by combining the clustering results from all three spectral regions, the ability to predict the correct age group for all samples increased (from 29-86% to 77-91%). This study is the first to compare several clustering methods on infrared spectra of bone. Fuzzy C-means clustering performed best, and its ability to study the degree of memberships of samples to each cluster might be beneficial in future studies of medical diagnostics.

Sputtering is a crucial technology in fields such as electric propulsion, materials processing and semiconductors. Modeling of sputtering is significant for improving thruster design and designing material processing control algorithms. In this study we use the hierarchical clustering analysis algorithm to perform cluster analysis on 17 descriptors related to sputtering. These descriptors are divided into four fundamental groups, with representative descriptors being the mass of the incident ion, the formation energy of the incident ion, the mass of the target and the formation energy of the target. We further discuss the possible physical processes and significance involved in the classification process, including cascade collisions, energy transfer and other processes. Finally, based on the analysis of the above descriptors, several neural network models are constructed for the regression of sputtering threshold E th, maximum sputtering energy E max and maximum sputtering yield SY max. In the regression model based on 267 samples, the four descriptor attributes showed higher accuracy than the 17 descriptors (R 2 evaluation) in the same neural network structure, with the 5×5 neural network structure achieving the highest accuracy, having an R 2 of 0.92. Additionally, simple sputtering test data also demonstrated the generalization ability of the 5×5 neural network model, the error in maximum sputtering yield being less than 5%.

In data mining, a combination of discovery of connected components of data by cluster analysis and capture of topological structure based on persistent homology attracted attention in recent years is effective. Especially in cluster analysis, k-means (KM) also referred to as hard c-means (HCM) and fuzzy c-means (FCM), which are representative methods and called objective-based clustering, assign data of each cluster to one representative point. These methods have an aspect of data compression. As a result, a large amount of data can be compressed and handled as representative points, so that the structure of the entire data can be known by grasping the topological structure of the representative points. Therefore, HCM and FCM have high affinity with persistent homology. In order to consider data mining methods using persistent homology, it is necessary that the mathematical property of filtration holds. However, here, there is the big problem that “topological structure made from HCM and FCM does not mathematically guarantee filtration”. In this paper, we propose a new objective-based clustering method to classify a dataset into clusters with topological structure with filtration. The proposed algorithm makes the cluster partition homotopy equivalent to the weighted $$\alpha$$ -complex. This enables data mining using clustering and capture of the topological structure by persistent homology.

In this paper an improved clustering analysis algorithm is proposed on the basis of randomly selected data in the CURE algorithm and the cluster centers setting in the K-NN algorithm. The combination of the two algorithms conquers the poor clustering accuracy in the CURE algorithm and the clustering deficiency on large data set in the K-NN algorithm. This paper first introduces the concept of clustering analysis and its real-life applications, then proceeds to describe the method of clustering analysis, and then introduces optimized CURE_KNN algorithm described in pseudo-code, at last the advantages of the algorithm are summarized.

In the context of the combination of industry and education, the construction of industrial colleges in vocational colleges can drive the scientific development of specialty settings in colleges and universities, and promote the way for colleges to expand students' practical teaching under the teaching of theoretical knowledge, and it is also an effective way for students to stimulate their learning enthusiasm and innovation enthusiasm. Colleges and universities can increase the direction and characteristics of specialist settings in colleges while enhancing instructors' professional level through school-business collaboration, and growing measures of talent training in colleges and universities plays a significant guiding role. The way to set up industrial colleges in vocational colleges reflects the development characteristics of talent training mode in the new era, and it is also an effective way to meet the practical training of students and the actual needs of society. It is a new school running mode of transforming productivity, cooperation, and mutual benefit, which is very worthy of promotion and development. This paper analyzes the problems existing in the construction of industrial colleges in vocational colleges in China and finds out the corresponding solutions. A path method of industrial college construction in vocational colleges based on the cluster analysis algorithm is proposed. The validity of this model is verified by experiments, which lays a foundation for the construction of industrial colleges in vocational colleges.

Similarity and cluster analysis are important aspects for analyzing microarray data. Based on our perspective of viewing microarrays as time series data, both similarity analysis and cluster analysis are carried out through indexing on time series data using R*-Trees. We have developed algorithms for similarity and cluster analysis on microarray data, and conducted experimental studies and comparative studies. First, our study shows that principle components analysis (PCA) has superiority over several other methods (such as DFT and PAA) as far as distance conservation is concerned. A similarity analysis tool based on PCA has been developed, which is able to explore less R*-Tree nodes before finding its similar counterparts and returns less false positives than other methods. In addition, we also extend R*-Tree's application to cluster analysis. With the aid of R*-Tree indexing, two clustering algorithms. KMeans-R and Hierarchy-R, are proposed as an improved version of K-Means and hierarchical clustering, respectively. Experiments for similarity search and cluster analysis based on proposed algorithms have been carried out and have shown favorable results. Experiments related to yeast cell cycle dataset are reported in this paper.

In this research, a grid-connected hybrid renewable electricity system was studied with the aim of providing the required electricity to the buildings of Yazd city in Iran. All possible structures of the proposed system were simulated using the genetic algorithm and TRNSYS software. Then the final stable population was categorised into three clusters using the statistical quartiles. Based on the results, only about 1% of all investigated structures were optimum considering energy cost and CO2 emission. Investigating the best-identified structures showed that it could decrease the CO2 emission by about 92% and energy cost by about 35% compared to the case where only the grid electricity was used. Moreover, wind energy had the highest priority, and using batteries was not an appropriate solution for the study area. The results showed that identifying an optimum cluster can be used efficiently instead of as a unique solution for the studied system. Highlights Hybrid electricity supply system. Combination of Cluster Analysis and Genetic algorithm for multi-objective optimisation. Simulating the control strategies for energy systems.

In order to resist network malicious attacks, this paper briefly introduced the network intrusion detection model and K-means clustering analysis algorithm, improved them, and made a simulation analysis on two clustering analysis algorithms on MATLAB software. The results showed that the improved K-means algorithm could achieve central convergence faster in training, and the mean square deviation of clustering center was smaller than the traditional one in convergence. In the detection of normal and abnormal data, the improved K-means algorithm had higher accuracy and lower false alarm rate and missing report rate. In summary, the improved K-means algorithm can be applied to network intrusion detection.