Reverse Engineering the Fly Brain Using FlyCircuit Database

Modified FDP cluster algorithm and its application in protein conformation clustering analysis

The first synchronization clustering (SynC) algorithm based on an extensive Kuromato model was presented in 2010. In 2017, an effective synchronization clustering (ESynC) algorithm, inspired by SynC algorithm and a linear version of Vicsek model, was proposed. When facing complex data distributions, ESynC algorithm may regard an irregular and whole cluster as some micro-clusters. To conquer this shortcoming, a combined clustering algorithm based on ESynC algorithm and a merging judgement process of micro-clusters (CESynC) is presented. CESynC algorithm first uses ESynC algorithm to detect clusters or micro-clusters, then merges those conjoint micro-clusters by using a merging judgement process. For some datasets that ESynC and SynC cannot detect correct clusters, CESynC can capture natural clusters. From the simulation experiments, we observe that CESynC can get better (or the same) clustering results than (or as) that of ESynC in many cases. We also observe that the clustering results of CESynC and ESynC are often better than that of SynC. Therefore, we can say CESynC can often obtain better clustering quality than ESynC and SynC in some kinds of datasets. Further comparison experiments with some classical clustering algorithms demonstrate the clustering effect of CESynC.

Recently, a variety of data acquisition methods leads to the description of the same thing from different angles, which induces a large number of multi-view data. Clustering multi-view data in large-scale data analysis is of great significance and very challenging. In this paper, we present a new robust clustering algorithm, the Multi-view Fuzzy K-means clustering (MFKC) algorithm. In MFKC, a fuzzy membership method is used and optimized to describe the probability of a sample belonging to certain clusters. Compared with the hard partition based multi-view clustering algorithms, MFKC can effectively describe the fuzzy relationship between multi-view data and all clusters, and has better interpretability for the clustering results. In addition, MFKC also sets different weights for different views. By optimizing the multi-view clustering objective function, we obtain the optimal weigh of each view to reflect the importance of the view. The feasibility and effectiveness of the proposed algorithm is validated through various experimental comparisons.

Use of a fuzzy granulation–degranulation criterion for assessing cluster validity

Keeping networks security has never been such an imperative task as today. In this paper, by combining particle swarm optimization and clustering algorithm, a new detection method, Intrusion Detection based on Unsupervised Clustering and PSO algorithm (IDCPSO), is proposed. Particle swarm optimization algorithm is used to optimize the clustering results and obtain the optimal detection result. IDCPSO needs unlabeled data for training and automatically establish clusters and detect intruders by labeling normal and abnormal groups. Computer experiment results with KDD cup show that this algorithm is effective for intrusion detection.

1. The fast extensor tibiae motor neuron makes direct excitatory central connections with the posterior group of flexor tibiae motor neurons in the locust metathoracic ganglion. The flexor group has a slow, a fast, and an intermediate motor neuron. The motor neurons are involved in the motor program for kicking and jumping, the defensive and escape behaviors of the locust. An antidromic action potential in fast extensor tibiae motor neuron (FETi) results in a monosynaptic, glutamatergic excitatory postsynaptic potential (EPSP) in each of the flexor motor neurons. 2. A train of 10 antidromic spikes in FETi at frequencies of 1-20 Hz resulted in depression of the amplitude of the EPSP in each of the flexor motor neurons. The depression was not significantly different in the different flexor motor neurons. The depression was greater with higher frequency stimulation and was reduced in low calcium saline. 3. After stimulation at 20 Hz, the EPSP amplitude was depressed by approximately 80%. This did not change when the number of stimuli was increased to 20, when stimulation was done in high calcium saline, or when the frequency of stimulation was increased to 50 or 100 Hz. The recovery from depression was greater after 20-Hz stimulation than at lower frequencies, although the recovery was reduced when the number of stimuli was increased, and also in high calcium saline. 4. In normal saline the depression of the EPSP amplitude was associated with a reduction of the presynaptic spike amplitude at frequencies of > or = 5 Hz. In tetraethylammonium (TEA) saline the width of a TEA-broadened spike was also reduced. The reduction in spike amplitude and spike width correlated with the depression of the EPSP. 5. Certain of these results are consistent with a depletion model of synaptic depression, whereas others are not consistent with this model. The depression may be partly due to an initial depletion of transmitter stores, and partly to modulation of the presynaptic action potential that reduces calcium entry, and therefore transmitter release. The significance of the depression on the motor program for kicking and jumping is discussed.

Simulated annealing spectral clustering algorithm for image segmentation

Based on the traditional grid density clustering algorithm, proposing A Data Stream Clustering Algorithm Based on Density and Extended Grid(DEGDS). The algorithm combines the advantages of grid clustering algorithm and density clustering algorithm, by improving the defects of clustering parameters by artificially set, get any shape of the cluster. The algorithm uses the local density of each sample point and the distance from the other sample points, determining the number of clustering centers in the grid, and realizing the automatic determination of the clustering center, which avoids the influence of improper selection of initial centroid on clustering results. And in the process of combining the Spark parallel framework for partitioning the data to achieve its parallelization. For data points clustered outside the grid, the clustering within the grid has been effectively expanded by extending the grid, to ensure the accuracy of clustering. Introduced density estimation is connected and grid boundaries to merging grid, saving memory consumption. Using the attenuation factor to incremental update grid density, reflect the evolution of spatial data stream. The experimental results show that compared with the traditional clustering algorithm, the DEGDS algorithm has a large performance improvement in accuracy and efficiency, and can be effectively for large data clustering.

<div class="section abstract"><div class="htmlview paragraph">Driver steering feature clustering aims to understand driver behavior and the decision-making process through the analysis of driver steering data. It seeks to comprehend various steering characteristics exhibited by drivers, providing valuable insights into road safety, driver assistance systems, and traffic management. The primary objective of this study is to thoroughly explore the practical applications of various clustering algorithms in processing driver steering data and to compare their performance and applicability. In this paper, principal component analysis was employed to reduce the dimension of the selected steering feature parameters. Subsequently, K-means, fuzzy C-means, the density-based spatial clustering algorithm, and other algorithms were used for clustering analysis, and finally, the Calinski-Harabasz index was employed to evaluate the clustering results. Furthermore, the driver steering features were categorized into lateral and longitudinal categories. Different clustering algorithms were selected for clustering analysis to assess potential differences between lateral and longitudinal steering features and to determine the applicability of various algorithms for this purpose. In the comparative analysis, we comprehensively evaluate the performance of various algorithms in terms of clustering quality, interpretability, computational efficiency, and applicability. Selecting algorithms suitable for specific tasks and datasets is crucial to enhancing computational efficiency and the quality of clustering results. Through the comparative analysis of the driver steering feature clustering algorithm, a deeper understanding of the steering behavior mode can be obtained, providing support for optimizing the driver assistance system, advancing automatic driving technology, enhancing the driving experience, optimizing traffic management, and ultimately realizing a more intelligent and safer road traffic system.</div></div>

In this paper, the method that measuring dataset of knitted yarns is clustered using improving fuzzy kernel c-Means (FKCM) clustering algorithm is proposed. In FKCM clustering algorithm, the data of low dimension input space is mapped to high dimension feature space, FCM clustering algorithm is performed in feature space, then the constraint optimization distance matrix and membership matrix of testing samples are computed by utilizing iterative algorithm, and the clustering result can be acquired according maximum membership principle. Subsequently, the Kernel F cluster validity index is designed for seeking the fitness cluster number and the corresponding relationship model of clusters sequence number and quality grades is constructed. Improving FKCM clustering algorithm is more efficient than other clustering algorithms, and clustering result can provide the training samples for constructing quality grades and clusters recognition function of new samples. The combination of improving FKCM and KF index provides an efficient data analysis method for multi-index dataset.

There is a trend in recent machine learning community to construct a nonlinear version of a linear algorithm using the 'kernel method', e.g. Support Vector Machines (SVMs), kernel principal component analysis, kernel fisher discriminant analysis and the recent kernel clustering algorithms. In unsupervised clustering algorithms using kernel method, typically, a nonlinear mapping is used first to map the data into a potentially much higher feature space, where clustering is then performed. A drawback of these kernel clustering algorithms is thatthe clustering prototypes lie in high dimensional feature space and hence lack clear and intuitive descriptions unless using additional projection approximation from the feature to the data space as done in the existing literatures. In this paper, a novel clustering algorithm using the 'kernel method' based on the classical fuzzy clustering algorithm (FCM) is proposed and called as kernel fuzzy c-means algorithm (KFCM). KFCM adopts a new kernel-induced metric in the data space to replace the original Euclidean norm metric in FCM and the clustered prototypes still lie in the data space so that the clustering results can be reformulated and interpreted in the original space. Our analysis shows that KFCM is robust to noise and outliers and also tolerates unequal sized clusters. And finally this property is utilized to cluster incomplete data. Experiments on two artificial and one real datasets show that KFCM hasbetter clustering performance and more robust than several modifications of FCM for incomplete data clustering.

This paper proposed the SVD (singular value decomposition) clustering algorithm to cluster wind turbines into some group for a large offshore wind farm, in order to reduce the high‐dimensional problem in wind farm power control and numerical simulation. Firstly, wind farm wake relationship matrixes are established considering the wake effect in an offshore wind farm, and the SVD of wake relationship matrixes is used to cluster wind turbines into some groups by the fuzzy clustering algorithm. At last, the Horns Rev offshore wind farm is analyzed to test the clustering algorithm, and the clustering result and the power simulation show the effectiveness and feasibility of the proposed clustering strategy.

How to dig out the business perspectives and market rules behind commodity transaction data, explore the relationship between commodities, so as to more scientifically and rationally classify and promote commodity categories and improve commodity sales performance for e-commerce companies has become a recent research hotspot. To this end, this paper proposes to use clustering algorithm to explore the hidden laws of commodity-related big data. This article first consults a large amount of information through the literature survey method, systematically summarizes the relevant theoretical knowledge of the association rule method and clustering algorithm and gives a detailed introduction to its application in the commodity association big data mining. The research in this area has laid a sufficient theoretical foundation; after that, the Apriori algorithm in the association rules and the K-means algorithm in the clustering algorithm were used to carry out the fast clustering algorithm experiment of the commodity-related big data sparse network and the commodity transaction data was introduced in detail. The process of association analysis and cluster analysis; then taking China’s well-known e-commerce platform Jingdong Mall as an example, by investigating the commodity transaction records of Jingdong Mall in the 4th week of July, the association and cluster analysis of its commodity transaction data were found. Among them, mobile phones and Bluetooth earphone, laptops and Bluetooth earphone, laptops and hard disks have the highest correlation and their confidence thresholds have reached 25%, 35 and 40% respectively. Finally, when the clustering results were tested, they were also found in the store. Strengthening the push and shopping guide of highly relevant product combinations on the website pages will increase the sales of products.

Mixed datasets consist of both numeric and categorical attributes. Various k-means-based clustering algorithms have been developed for these datasets. Generally, these algorithms use random partition as a starting point, which tends to produce different clustering results for different runs. In this paper, we propose, initKmix, a novel algorithm for finding an initial partition for k-means-based clustering algorithms for mixed datasets. In the initKmix algorithm, a k-means-based clustering algorithm is run many times, and in each run, one of the attributes is used to create initial clusters for that run. The clustering results of various runs are combined to produce the initial partition. This initial partition is then used as a seed to a k-means-based clustering algorithm to cluster mixed data. Experiments with various categorical and mixed datasets showed that initKmix produced accurate and consistent results, and outperformed the random initial partition method and other state-of-the-art initialization methods. Experiments also showed that k-means-based clustering for mixed datasets with initKmix performed similar to or better than many state-of-the-art clustering algorithms for categorical and mixed datasets.

We adopt clustering algorithm to improve segmentation accuracy. In this paper, 3D laser scanning platform was built to obtain the spatial 3D point cloud data. And then we extracted the point cloud data for two planar features. K-means algorithm, density-based clustering algorithm and density peak clustering algorithm were employed to split the 3D point cloud of the two planes. After clustering, we compared and analyzed the clustering results of the three clustering algorithms. More importantly, we also found that for peak density clustering, the threshold value is related to its sensitivity to noise points. After fitting the two planes, the verticality of two planes was also calculated. We analyzed the results and summarized the criterion for selecting thresholds.