Shared Neighbor Research Articles

Effective Utilization of Shared Nearest Node for Message Diffusion in Social Network Using Dbscan

The social networking service has been enormously used among various people to share information or to build social relationship between acquaintances and other people as well. This term is used to describe a social structure where many users can bring forth their perspective on certain global information or imbalances that has been occurred over centuries. The goal of Information diffusion is to spread messages over a network with a lesser time complexity and efficient accessibility. Here, to ease the process of message diffusion in social networking, we are finding overlapping nodes between commonly Shared Nearest nodes and aid in spreading the information more appropriately by reducing the complexity in the existing system and promoting an efficient level of performance. Density-based clustering is a relevant method we have used to trace shared nearest neighbor node. Also, we provide security for the data that is being diffused by implementing the RSA security algorithm and providing the security key along with the information and hence the group of people who are eligible to access the data with the security key can only access the data. Hence the information is being diffused evenly to each part in the cluster with less time complexity and efficiency.

On the number of reflexive and shared nearest neighbor pairs in one-dimensional uniform data

For a random sample of points in R, we consider the number of pairs whose members are nearest neighbors NNs to each other and the number of pairs sharing a common NN. The pairs of the first type are called reflexive NNs, whereas the pairs of the latter type are called shared NNs. In this article, we consider the case where the random sample of size n is from the uniform distribution on an interval. We denote the number of reflexive NN pairs and the number of shared NN pairs in the sample by Rn and Qn, respectively. We derive the exact forms of the expected value and the variance for both Rn and Qn, and derive a recurrence relation for Rn which may also be used to compute the exact probability mass function pmf of Rn. Our approach is a novel method for finding the pmf of Rn and agrees with the results in the literature. We also present SLLN and CLT results for both Rn and Qn as n goes to infinity.

Single cell clustering based on cell-pair differentiability correlation and variance analysis.

The rapid advancement of single cell technologies has shed new light on the complex mechanisms of cellular heterogeneity. Identification of intercellular transcriptomic heterogeneity is one of the most critical tasks in single-cell RNA-sequencing studies. We propose a new cell similarity measure based on cell-pair differentiability correlation, which is derived from gene differential pattern among all cell pairs. Through plugging into the framework of hierarchical clustering with this new measure, we further develop a variance analysis based clustering algorithm 'Corr' that can determine cluster number automatically and identify cell types accurately. The robustness and superiority of the proposed algorithm are compared with representative algorithms: shared nearest neighbor (SNN)-Cliq and several other state-of-the-art clustering methods, on many benchmark or real single cell RNA-sequencing datasets in terms of both internal criteria (clustering number and accuracy) and external criteria (purity, adjusted rand index, F1-measure). Moreover, differentiability vector with our new measure provides a new means in identifying potential biomarkers from cancer related single cell datasets even with strong noise. Prognosis analyses from independent datasets of cancers confirmed the effectiveness of our 'Corr' method. The source code (Matlab) is available at http://sysbio.sibcb.ac.cn/cb/chenlab/soft/Corr--SourceCodes.zip. Supplementary data are available at Bioinformatics online.

A novel clustering method to identify cell types from single cell transcriptional profiles

With the advancement of the high throughput single cell techniques, transcriptomics data generation at the single cell level becomes very easy. Analysis of this single cell expression values can reveal lots of unprecedented information about complex cellular heterogeneity and tissue composition. To date, different statistical methods are applied to analyze expression data at the cellular level, but there is still pretty much scope for the development of new bioinformatics tools. In this article, a graph theoretic clustering algorithm is proposed to identify cellular states from single cell gene expression data. The proposed algorithm first generates a shared nearest neighbor graph from the single cell RNA-seq dataset and then applies minimum spanning tree based clustering method to cluster the graph nodes. To compare our proposed algorithm’s performance with other unsupervised clustering methods, we used three real scRNA-seq datasets (human cancer cells, human embryonic cells and mouse embryonic cells). From the comparison result, it is evident that the proposed algorithm outperforms other standard single cell analysis methods.

A data mining framework for environmental and geo-spatial data analysis

Mining geo-spatial data is an important task in many application domains, such as environmental science, geographic information science, and social networks. In this paper, we introduce a data mining framework, which includes pre-processing of environmental and geo-spatial data, geo-spatial data mining techniques, and visual analysis of environmental and geo-spatial data. In particular, we propose new density-based clustering algorithms to identify interesting distribution patterns from geo-spatial data, a change pattern discovery technique to detect dynamic change patterns within spatial clusters, and a post-processing technique to extract interesting patterns and useful knowledge from geo-spatial data. Our density-based clustering algorithms are based on the well-established density-based shared nearest neighbor clustering algorithm, which can find clusters of different shape, size, and densities in high-dimensional data. The post-processing analysis technique allows automatic screening of interesting spatial clusters. The change pattern discovery algorithm is able to detect and analyze dynamic patterns of changes within spatial clusters. This paper focuses on developing a framework integrating a sequence of data mining process including clustering algorithm, analysis technique and pattern changing discovery algorithm. In contrast to previous works in this area, our approaches can cluster and analyze dynamically evolved complex objects, i.e., polygons. We evaluate the effectiveness of our techniques through a challenging real case study involving ozone pollution events in the Houston–Galveston–Brazoria area. The experimental results show that our approaches can discover interesting patterns and useful information from geo-spatial air-quality data.

Automatic malware classification and new malware detection using machine learning

The explosive growth of malware variants poses a major threat to information security. Traditional anti-virus systems based on signatures fail to classify unknown malware into their corresponding families and to detect new kinds of malware programs. Therefore, we propose a machine learning based malware analysis system, which is composed of three modules: data processing, decision making, and new malware detection. The data processing module deals with gray-scale images, Opcode n-gram, and import functions, which are employed to extract the features of the malware. The decision-making module uses the features to classify the malware and to identify suspicious malware. Finally, the detection module uses the shared nearest neighbor (SNN) clustering algorithm to discover new malware families. Our approach is evaluated on more than 20 000 malware instances, which were collected by Kingsoft, ESET NOD32, and Anubis. The results show that our system can effectively classify the unknown malware with a best accuracy of 98.9%, and successfully detects 86.7% of the new malware.

Enhanced shared nearest neighbor clustering approach using fuzzy for teleconnection analysis

Massive amount of Earth science data open an unprecedented opportunity to discover potentially valuable information. Earth science data are complex, nonlinear, high-dimensional data, and the sparsity of data in high-dimensional space poses major challenge in clustering of the data. Shared nearest neighbor clustering (SNN) algorithm is one of the well-known and efficient methods to handle high-dimensional spatiotemporal data. The SNN clustering method does not cluster all the data forming rigid boundary selection. This paper reports fuzzy shared nearest neighbor (FSNN) algorithm which is an enhancement of the SNN clustering method that has the capability of handling the data lying in the boundary regions by means of a fuzzy concept. The clusters obtained can be characterized by the cluster centroid, which summarizes the behavior of the ocean points in the cluster. The statistical measure is used to find the significant relation between the cluster centroids and the existing climate indices. In this study, correlation measure is used to find the significant pattern, such as teleconnection or dipole. The experimentation is performed on Indian continent latitude range $$7.5^{\circ }{-}37.5^{\circ }\hbox {N}$$ and longitude range $$67.5^{\circ }{-}97.5^{\circ }\hbox {E}$$ . Extensive experiments are carried out to compare the proposed approach with existing clustering methods such as K-means, fuzzy C-means and SNN. The proposed method, FSNN algorithm, not only handles the data lying in the overlapping region, but it also finds more compact and well-separated clusters. FSNN shows better results in terms of finding a significant correlation between cluster centroids and existing climate indices and validated by ground truth dataset.

3D Point Cloud Registration Based on Refined ICP Algorithm

Three-dimension (3D) digitization of human face is of great value for medical plastic surgery, video making, virtual reality, and so on. Subject to the restriction of 3D scanning equipment, a single angle of scanning measurement only obtains partial information of a face. In this paper, a multi-Kinect measurement system was proposed for the scanning of human face from multiple angles simultaneously. Firstly, different sides of human face point clouds scanned by three Kinect sensors were registered by an ICP algorithm to form a full human face point cloud. Then, a bilateral filtering algorithm was refined to remove noise in the point cloud. Finally, a point feature histogram and a refined shared nearest neighbor cluster algorithm were applied to improve the iterative speed and efficiency of the ICP algorithm. Experiment shows that the refined point cloud registration algorithm is efficient for 3D digitization of full human face.

Locally adaptive multiple kernel k-means algorithm based on shared nearest neighbors

Most of multiple kernel clustering algorithms aim to find the optimal kernel combination and have to calculate kernel weights iteratively. For the kernel methods, the scale parameter of Gaussian kernel is usually searched in a number of candidate values of the parameter and the best is selected. In this paper, a novel locally adaptive multiple kernel k-means algorithm is proposed based on shared nearest neighbors. Our similarity measure meets the requirements of the clustering hypothesis, which can describe the relations between data points more reasonably by taking local and global structures into consideration. We assign to each data point a local scale parameter and combine the parameter with shared nearest neighbors to construct kernel matrix. According to the local distribution, the local scale parameter of Gaussian kernel is generated adaptively. Experiments show that the proposed algorithm can effectively deal with the clustering problem of data sets with complex structure or multiple scales.

Planning of Fast EV Charging Stations on a Round Freeway

A novel planning method of fast electric vehicle (EV) charging stations on a round freeway was developed, considering the spatial and temporal transportation behaviors. A spatial and temporal model based on the origin-destination (OD) analysis was developed to obtain all the EV charging points (the location on the round freeway that an EV needs recharging due to the low battery capacity). Based on a shared nearest neighbor (SNN) clustering algorithm, a location determination model was developed to obtain the specific locations for EV charging stations with their service EV clusters. A capacity determination model based on the queuing theory was proposed to determine the capacity of each EV charging station. The round-island freeway in Hainan Island of China was employed as a test system to illustrate the planning method. Simulation results show that the developed planning method can not only accurately determine the most suitable locations for EV fast charging stations considering the travelling convenience of EV users, but also minimize the sum of waiting cost and charger cost.

Robust Similarity Measure for Spectral Clustering Based on Shared Neighbors

Spectral clustering is a powerful tool for exploratory data analysis. Many existing spectral clustering algorithms typically measure the similarity by...

Multi-objective optimization of shared nearest neighbor similarity for feature selection

A new unsupervised feature selection algorithm, based on the concept of shared nearest neighbor distance between pattern pairs, is developed. A multi-objective framework is employed for the preservation of sample similarity, along with dimensionality reduction of the feature space. A reduced set of samples, chosen to preserve sample similarity, serves to reduce the effect of outliers on the feature selection procedure while also decreasing computational complexity. Experimental results on six sets of publicly available data demonstrate the effectiveness of this feature selection strategy. Comparative study with related methods based on different evaluation indices have demonstrated the superiority of the proposed algorithm.

Discovery of Intermediary Genes between Pathways Using Sparse Regression.

The use of pathways and gene interaction networks for the analysis of differential expression experiments has allowed us to highlight the differences in gene expression profiles between samples in a systems biology perspective. The usefulness and accuracy of pathway analysis critically depend on our understanding of how genes interact with one another. That knowledge is continuously improving due to advances in next generation sequencing technologies and in computational methods. While most approaches treat each of them as independent entities, pathways actually coordinate to perform essential functions in a cell. In this work, we propose a methodology based on a sparse regression approach to find genes that act as intermediary to and interact with two pathways. We model each gene in a pathway using a set of predictor genes, and a connection is formed between the pathway gene and a predictor gene if the sparse regression coefficient corresponding to the predictor gene is non-zero. A predictor gene is a shared neighbor gene of two pathways if it is connected to at least one gene in each pathway. We compare the sparse regression approach to Weighted Correlation Network Analysis and a correlation distance based approach using time-course RNA-Seq data for dendritic cell from wild type, MyD88-knockout, and TRIF-knockout mice, and a set of RNA-Seq data from 60 Caucasian individuals. For the sparse regression approach, we found overrepresented functions for shared neighbor genes between TLR-signaling pathway and antigen processing and presentation, apoptosis, and Jak-Stat pathways that are supported by prior research, and compares favorably to Weighted Correlation Network Analysis in cases where the gene association signals are weak.

Finding Sequence Clusters: A Shared Near Neighbors Approach

Sequence clustering is one of most fundamental topics which can be applied in various research field. Most of previous work on sequence clustering is dedicated to the single- label clustering in which the whole similarity of equal-length sequence is considered and measured by Euclidean distance function. However, intrinsic properties behind sequence demand the multi-label clustering. In addition, the Euclidean distance in the high dimensional space introduce the problem of dimensionality curse. Therefore, in this paper, we employ the concept of shared near neighbors (SNN), for sequence similarity, which will be integrated in the multi-label clustering process. Given a set of sequences, in our approach, we first apply the sliding window technique and the DCT mapping on sequences to obtain feature vectors. Those feature vectors, associated with the SNN similarity, are further grouped by applying the graph-based clustering and the hierarchical clustering, respectively. We also design a validity measure and perform experiments to show the efficiency and effectiveness of our approach. Meanwhile, those feature vectors are also approximated by the minimum bounding rectangles (MBR). Due to the less amount of MBRs, compared to all feature vectors, the computational complexity can be reduced accordingly without compromising clustering validity.

A shared neighbor conditional autoregressive model for small area spatial data

The use of conditional autoregressive (CAR) models for spatial effects is commonplace, especially when dealing with aggregated count data in health studies. CAR models are convenient and relatively easy to implement but suffer from the fact that they have limited flexibility in modeling correlation. We introduce a new CAR model that can accommodate different neighborhood features (including shared neighbors). Further, we examine via simulation how this model performs in comparison with standard CAR models. We also consider the application to a small area health data example. Copyright © 2015 John Wiley & Sons, Ltd.

An Adaptive Spectral Clustering Algorithm Based on the Importance of Shared Nearest Neighbors

The construction of a similarity matrix is one significant step for the spectral clustering algorithm; while the Gaussian kernel function is one of the most common measures for constructing the similarity matrix. However, with a fixed scaling parameter, the similarity between two data points is not adaptive and appropriate for multi-scale datasets. In this paper, through quantitating the value of the importance for each vertex of the similarity graph, the Gaussian kernel function is scaled, and an adaptive Gaussian kernel similarity measure is proposed. Then, an adaptive spectral clustering algorithm is gotten based on the importance of shared nearest neighbors. The idea is that the greater the importance of the shared neighbors between two vertexes, the more possible it is that these two vertexes belong to the same cluster; and the importance value of the shared neighbors is obtained with an iterative method, which considers both the local structural information and the distance similarity information, so as to improve the algorithm’s performance. Experimental results on different datasets show that our spectral clustering algorithm outperforms the other spectral clustering algorithms, such as the self-tuning spectral clustering and the adaptive spectral clustering based on shared nearest neighbors in clustering accuracy on most datasets.

Self-Tuning p-Spectral Clustering Based on Shared Nearest Neighbors

Cognitive computing needs to handle large amounts of data and information. Spectral clustering is a powerful data mining tool based on algebraic graph theory. Because of the solid theoretical foundation and good clustering performance, spectral clustering has aroused extensive attention of academia in recent years. Spectral clustering transforms the data clustering problem into the graph partitioning problem. Cheeger cut is an optimized graph partitioning criterion. To minimize the objective function of Cheeger cut, the eigen-decomposition of p-Laplacian matrix is required. However, the clustering results are sensitive to the selection of similarity measurement and the parameter p of p-Laplacian matrix. Therefore, we propose a self-tuning p-spectral clustering algorithm based on shared nearest neighbors (SNN-PSC). This algorithm uses shared nearest neighbors to measure the similarities of data couples and then applies fruit fly optimization algorithm to find the optimal parameters p of p-Laplacian matrix that leads to better data classification. Experiments show that SNN-PSC algorithm can produce more balanced clusters and has strong adaptability and robustness compared to traditional spectral clustering algorithms.

Identification of cell types from single-cell transcriptomes using a novel clustering method.

The recent advance of single-cell technologies has brought new insights into complex biological phenomena. In particular, genome-wide single-cell measurements such as transcriptome sequencing enable the characterization of cellular composition as well as functional variation in homogenic cell populations. An important step in the single-cell transcriptome analysis is to group cells that belong to the same cell types based on gene expression patterns. The corresponding computational problem is to cluster a noisy high dimensional dataset with substantially fewer objects (cells) than the number of variables (genes). In this article, we describe a novel algorithm named shared nearest neighbor (SNN)-Cliq that clusters single-cell transcriptomes. SNN-Cliq utilizes the concept of shared nearest neighbor that shows advantages in handling high-dimensional data. When evaluated on a variety of synthetic and real experimental datasets, SNN-Cliq outperformed the state-of-the-art methods tested. More importantly, the clustering results of SNN-Cliq reflect the cell types or origins with high accuracy. The algorithm is implemented in MATLAB and Python. The source code can be downloaded at http://bioinfo.uncc.edu/SNNCliq.

Testing Spatial Symmetry Using Contingency Tables Based on Nearest Neighbor Relations

We consider two types of spatial symmetry, namely, symmetry in the mixed or shared nearest neighbor (NN) structures. We use Pielou's and Dixon's symmetry tests which are defined using contingency tables based on the NN relationships between the data points. We generalize these tests to multiple classes and demonstrate that both the asymptotic and exact versions of Pielou's first type of symmetry test are extremely conservative in rejecting symmetry in the mixed NN structure and hence should be avoided or only the Monte Carlo randomized version should be used. Under RL, we derive the asymptotic distribution for Dixon's symmetry test and also observe that the usual independence test seems to be appropriate for Pielou's second type of test. Moreover, we apply variants of Fisher's exact test on the shared NN contingency table for Pielou's second test and determine the most appropriate version for our setting. We also consider pairwise and one-versus-rest type tests in post hoc analysis after a significant overall symmetry test. We investigate the asymptotic properties of the tests, prove their consistency under appropriate null hypotheses, and investigate finite sample performance of them by extensive Monte Carlo simulations. The methods are illustrated on a real-life ecological data set.

Robust multi-planar region detection for image mosaic based on hierarchical clustering mechanism

Planar structures exist widely in the images of various scenes, and the detection of planar regions is important in many applications related to computer vision, such as image mosaic and three-dimensional reconstruction. In this paper, a robust detection method for multi-planar regions is proposed. After the feature point pairs are extracted, their preference vectors are generated in similar conceptual space. By introducing the shared nearest neighbour in clustering procedure, the feature point pairs with smaller Jaccard distance and more shared nearest neighbours simultaneously are clustered into the same planar region. Because the relationship between the feature point pairs is considered, the accuracy of the inlier probability is high. Our method can detect multi-planar regions correctly without pre-determining the number of regions, and the corresponding clustered feature point pairs can be easily utilised for image mosaic. The experimental results show the effectiveness of the proposed method.