Application of Cluster Analysis to Aerometric Data

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.

Clustering analysis is an important approach of data mining. This paper presents an ANN-based clustering analysis algorithm with dynamic data window (DDW). Comparing with k-means algorithm merged in density-based and integrated clustering analysis algorithm, the new clustering analysis algorithm based on artificial neural networks and combining with DDW has more valuable in data mining. This algorithm can immensely avoid the effect on accumulation points from boundary points, and can automatically find representative accumulation points in all kings of shapes. Furthermore its applications in CIS will be discussed in the paper. Some analysis results show the significant improvement to ship-routing design with the clustering analysis algorithm based on ANN and DDW in database of CIS.

A detailed comparison of six multivariate algorithms is presented to analyze and generate Raman microscopic images that consist of a large number of individual spectra. This includes the segmentation algorithms for hierarchical cluster analysis, fuzzy C-means cluster analysis, and k-means cluster analysis and the spectral unmixing techniques for principal component analysis and vertex component analysis (VCA). All algorithms are reviewed and compared. Furthermore, comparisons are made to the new approach N-FINDR. In contrast to the related VCA approach, the used implementation of N-FINDR searches for the original input spectrum from the non-dimension reduced input matrix and sets it as the endmember signature. The algorithms were applied to hyperspectral data from a Raman image of a single cell. This data set was acquired by collecting individual spectra in a raster pattern using a 0.5-μm step size via a commercial Raman microspectrometer. The results were also compared with a fluorescence staining of the cell including its mitochondrial distribution. The ability of each algorithm to extract chemical and spatial information of subcellular components in the cell is discussed together with advantages and disadvantages.

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.

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.

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.

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 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.

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.

Fuzzy clustering analysis algorithm has good ability to solve fuzzy problems. Rough clustering analysis has good ability to solve these problems its prior knowledge is uncertain. But in the real world, there are many problems that not only are fuzzy but also are rough and uncertain; the paper combines the idea of these two algorithms. In order to improve correction of clustering, it imports attributes reduction algorithm to get importance of each attribute, and dynamically changes attribute weight by the importance. The new algorithm firstly computes fuzzy membership degree of every object and then estimates the object that belongs to lower approximation or upper approximation of one cluster. In the analysis process, the paper provides a new way to get the cluster centers, combining fuzzy and rough theory. From experiments of four UCI data sets, it is proved that the new algorithm is better effective.

The relationship between the physical parameters of pulsating vacuum sterilizer and the piping system states is analyzed based on cluster analysis and interpolation approximation algorithm. The cluster analysis is performed first, followed by the interpolation approximation of the cluster data. The algorithm provides a comprehensive sterilization efficacy evaluation, as well a feasible method to analyze the states of the system.

The present work aims to improve the comfort of architectural interior design and reduce indoor energy consumption. The Weight K-Nearest Neighborhood (WKNN) algorithm and Nondominated Sorting Genetic algorithm are proposed to locate and analyze the spatial location of indoor personnel and optimize the indoor energy consumption in combination with residential behavior. Firstly, the indoor human behavior data and energy-saving problems are analyzed based on residential behavior theory and architectural physics. The indoor positioning algorithm is proposed to identify the personnel activities to realize the optimization of indoor energy distribution. Secondly, mean filtering and cluster analysis are adopted to optimize sampling points' data and fingerprint database to eliminate data noise. Besides, the WKNN algorithm is used for Wireless Fidelity (Wi-Fi) indoor location fingerprint location. Then, aiming at the multiobjective optimization problem of building indoor energy consumption, the Nondominated Sorting Genetic algorithm obtains the optimal solution of the model. Combined with the indoor location information of personnel, the indoor heating and cooling system is monitored and distributed to reduce the energy consumption in the building while ensuring the living comfort of personnel. The test and simulation results demonstrate that the mean filtering algorithm can solve the room's fluctuation problem of Wi-Fi signals. The cluster analysis algorithm can eliminate the data noise of the fingerprint database and improve the positioning accuracy of the positioning algorithm. Moreover, the location result is independent of the number of nodes; the number of sampling points does not affect the location result. The positioning accuracy of the WKNN algorithm is 2 m, and the positioning error rate within 2 m is about 60%. Compared with other algorithms, the WKNN algorithm has better positioning accuracy and positioning stability. Therefore, the location algorithm designed here can be applied to indoor location optimization. This study provides a reference for optimizing buildings' indoor positioning and energy consumption.

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.

An ensemble clustering method is proposed that is based on a weight averaged co-association matrix. The ensemble includes various cluster analysis algorithms whose weights are calculated with the use of cluster validity indices. The properties of the ensemble are analyzed, a probabilistic model is described by which the relations between the characteristics of the ensemble and a quality estimate of a decision are determined, and a method is proposed for determining the optimal weights. The efficiency of the method is analyzed by statistical simulation.