Rough neighborhood graph: A method for proximity modeling and data clustering

Computational methods for identification of cell populations from polychromatic flow cytometry data are changing the paradigm of cytometry bioinformatics. Data clustering is the most common computational approach to unsupervised identification of cell populations from multidimensional cytometry data. However, interpretation of the identified data clusters is labor-intensive. Certain types of user-defined cell populations are also difficult to identify by fully automated data clustering analysis. Both are roadblocks before a cytometry lab can adopt the data clustering approach for cell population identification in routine use. We found that combining recursive data filtering and clustering with constraints converted from the user manual gating strategy can effectively address these two issues. We named this new approach DAFi: Directed Automated Filtering and Identification of cell populations. Design of DAFi preserves the data-driven characteristics of unsupervised clustering for identifying novel cell subsets, but also makes the results interpretable to experimental scientists through mapping and merging the multidimensional data clusters into the user-defined two-dimensional gating hierarchy. The recursive data filtering process in DAFi helped identify small data clusters which are otherwise difficult to resolve by a single run of the data clustering method due to the statistical interference of the irrelevant major clusters. Our experiment results showed that the proportions of the cell populations identified by DAFi, while being consistent with those by expert centralized manual gating, have smaller technical variances across samples than those from individual manual gating analysis and the nonrecursive data clustering analysis. Compared with manual gating segregation, DAFi-identified cell populations avoided the abrupt cut-offs on the boundaries. DAFi has been implemented to be used with multiple data clustering methods including K-means, FLOCK, FlowSOM, and the ClusterR package. For cell population identification, DAFi supports multiple options including clustering, bisecting, slope-based gating, and reversed filtering to meet various autogating needs from different scientific use cases. © 2018 International Society for Advancement of Cytometry.

Clustering of gene expression data simplifies subsequent data analyses and forms the basis of numerous approaches for biomarker identification, prediction of clinical outcome, and personalized therapeutic strategies. The most popular clustering methods such as K-means and hierarchical clustering are intuitive and easy to use, but they require arbitrary choices on their various parameters (number of clusters for K-means, and a threshold to cut the tree for hierarchical clustering). Human disease gene expression data are in general more difficult to cluster efficiently due to background (genotype) heterogeneity, disease stage and progression differences and disease subtyping; all of which cause gene expression datasets to be more heterogeneous. Spectral clustering has been recently introduced in many fields as a promising alternative to standard clustering methods. The idea is that pairwise comparisons can help reveal global features through the eigen techniques. In this paper, we developed a new recursive K-means spectral clustering method (ReKS) for disease gene expression data. We benchmarked ReKS on three large-scale cancer datasets and we compared it to different clustering methods with respect to execution time, background models and external biological knowledge. We found ReKS to be superior to the hierarchical methods and equally good to K-means, but much faster than them and without the requirement for a priori knowledge of K. Overall, ReKS offers an attractive alternative for efficient clustering of human disease data.

In view of the considerable applications of data clustering techniques in various fields, such as engineering, artificial intelligence, machine learning, clinical medicine, biology, ecology, disease diagnosis, and business marketing, many data clustering algorithms and methods have been developed to deal with complicated data. These techniques include supervised learning methods and unsupervised learning methods such as density-based clustering, K-means clustering, and K-nearest neighbor clustering. This book reviews recently developed data clustering techniques and algorithms and discusses the development of data clustering, including measures of similarity or dissimilarity for data clustering, data clustering algorithms, assessment of clustering algorithms, and data clustering methods recently developed for insurance, psychology, pattern recognition, and survey data.

Data clustering is an important research topic in data mining and signal processing communications. In all the data clustering methods, the subspace spectral clustering methods based on self expression model, e.g., the Sparse Subspace Clustering (SSC) and the Low Rank Representation (LRR) methods, have attracted a lot of attention and shown good performance. The key step of SSC and LRR is to construct a proper affinity or similarity matrix of data for spectral clustering. Recently, Laplacian graph constraint was introduced into the basic SSC and LRR and obtained considerable improvement. However, the current graph construction methods do not well exploit and reveal the non-linear properties of the clustering data, which is common for high dimensional data. In this paper, we introduce the classic manifold learning method, the Local Linear Embedding (LLE), to learn the non-linear structure underlying the data and use the learned local geometry of manifold as a regularization for SSC and LRR, which results the proposed LLE-SSC and LLE-LRR clustering methods. Additionally, to solve the complex optimization problem involved in the proposed models, an efficient algorithm is also proposed. We test the proposed data clustering methods on several types of public databases. The experimental results show that our methods outperform typical subspace clustering methods with Laplacian graph constraint.

This paper presents a clustering method for nominal and numerical data based on rough set theory. We represent relative similarity between objects as a weighted sum of two types of distances: the Hamming distance for nominal data and the Mahalanobis distance for numerical data. On assigning initial equivalence relations to every object, modification of slightly different equivalence relations is performed to suppress excessive generation of categories. The optimal clustering result can be obtained by evaluating the cluster validity over all clusters generated with various values of similarity thresholds. After classification has been performed, features of each class are extracted based on the concept of value reduct. Experimental results on artificial data and amino acid data show that this method can deal well with both types of attributes.

The use of the standardised testing notation, Testing and Test Control Notation (TTCN-3) [5] language has increased continuously over the last years. Many test suites of large sizes covering different domains exist. Therefore, it becomes important to provide the TTCN-3 community with methods and tools to evaluate the quality of tests. This paper presents the idea of evaluating the quality of the test data stimuli by using a data clustering method and measuring the coverage related to data clusters. A cluster contains stimuli which are considered similar for the system under test (SUT) behaviour; that means that each stimuli within a cluster should provide similar results from the test point of view.

Clustering-based segmentation algorithm is one of the common methods in image segmentation. However, how to avoid the influence of the initialization and the singular value is a problem we have to face. For overcoming this problem, we proposed a new image segmentation technology which is based on the AIC criterion and not affected by the singular values. The simulation results show that the method has good performance on stability and accuracy. Image segmentation is the key step in image analysis of computer vision and image processing field. It has been widely applied in various areas such as in the process of automating production, sensing images and biomedical images. The main goal of image segmentation is to simplify an image into segments that have a strong correlation with objects in the real world. The existing segmentation algorithm can be generally divided into three classes: edge detection, region- based segmentation and data clustering. Edge detection method relies on the edges of the object with background and the other objects. For the images with noise, it is difficult to separate the boundary and the target background (1). Region- based segmentation method segments an image into several meaningful sub-domains that are non-overlapping and same- nature. But it may lead to the over-segmentation, and has to combine with other methods to use (2). Data clustering method is based on the whole image and considers the distance between each data. Advantages of data clustering method are low complexity and easy to implement. Disadvantages are sensitive to the noise and selection of the initial centroids (3). Data clustering is one of the common techniques in image segmentation. Its purpose is to cluster pixels into several parts (regions) according to the feature of image. Based on the properties of clustering algorithm, researchers have proposed various image segmentation algorithm, such as based on hierarchical clustering, based on K-means clustering, based on fuzzy clustering, based on mean shift, and so on (3-5). Clustering analysis is an unsupervised process of partitioning a data set into subsets of similar data objects. The elementary principle is as far as possible to increase the difference between categories and reduce the difference within the category. In the process of image segmentation based on data clustering, thus the objective is that the similar pixels are divided into the same regions and the non-similar pixels are divided into different regions. However, Two difficulties we must face are that how to determine the cluster centers and avoid influence of noise. In this paper, a new image segmentation algorithm is proposed based on the AIC criterion. The new segmentation technology can achieve an ideal performance and not affected by the initialization of centroids and the singular values. The simulation results verify the efficiency of proposed algorithm.

Model-based clustering methods for continuous data are well established and commonly used in a wide range of applications. However, model-based clustering methods for categorical data are less standard. Latent class analysis is a commonly used method for model-based clustering of binary data and/or categorical data, but due to an assumed local independence structure there may not be a correspondence between the estimated latent classes and groups in the population of interest. The mixture of latent trait analyzers model extends latent class analysis by assuming a model for the categorical response variables that depends on both a categorical latent class and a continuous latent trait variable; the discrete latent class accommodates group structure and the continuous latent trait accommodates dependence within these groups. Fitting the mixture of latent trait analyzers model is potentially difficult because the likelihood function involves an integral that cannot be evaluated analytically. We develop a variational approach for fitting the mixture of latent trait models and this provides an efficient model fitting strategy. The mixture of latent trait analyzers model is demonstrated on the analysis of data from the National Long Term Care Survey (NLTCS) and voting in the U.S. Congress. The model is shown to yield intuitive clustering results and it gives a much better fit than either latent class analysis or latent trait analysis alone.


 
 
 
 Abstract
 This paper presents a novel approach to generating document summaries using data mining and clustering techniques, specifically K-means clustering and bisecting K-means clustering algorithms. With the exponential growth of textual data, there is an increasing need for efficient and accurate summarization techniques to aid users in understanding the key information within large collections of documents. This study explores the potential of data mining and clustering methods in extracting salient features from textual data and producing high-quality summaries. By applying K-means clustering and bisecting K-means clustering algorithms to the preprocessed textual data, the proposed approach groups similar sentences together and selects the most representative sentences from each cluster to form the final summary. The performance of the proposed method is evaluated using standard evaluation metrics, such as precision, recall, and F1-score, and compared with existing summarization techniques. The results demonstrate that the combination of data mining and clustering techniques provides a promising solution for generating accurate and concise document summaries, with potential applications in various domains, such as news aggregation, scientific literature summarization, and social media content analysis.
 
 
 

BackgroundOne major goal of large-scale cancer omics study is to identify molecular subtypes for more accurate cancer diagnoses and treatments. To deal with high-dimensional cancer multi-omics data, a promising strategy is to find an effective low-dimensional subspace of the original data and then cluster cancer samples in the reduced subspace. However, due to data-type diversity and big data volume, few methods can integrative and efficiently find the principal low-dimensional manifold of the high-dimensional cancer multi-omics data.ResultsIn this study, we proposed a novel low-rank approximation based integrative probabilistic model to fast find the shared principal subspace across multiple data types: the convexity of the low-rank regularized likelihood function of the probabilistic model ensures efficient and stable model fitting. Candidate molecular subtypes can be identified by unsupervised clustering hundreds of cancer samples in the reduced low-dimensional subspace. On testing datasets, our method LRAcluster (low-rank approximation based multi-omics data clustering) runs much faster with better clustering performances than the existing method. Then, we applied LRAcluster on large-scale cancer multi-omics data from TCGA. The pan-cancer analysis results show that the cancers of different tissue origins are generally grouped as independent clusters, except squamous-like carcinomas. While the single cancer type analysis suggests that the omics data have different subtyping abilities for different cancer types.ConclusionsLRAcluster is a very useful method for fast dimension reduction and unsupervised clustering of large-scale multi-omics data. LRAcluster is implemented in R and freely available via http://bioinfo.au.tsinghua.edu.cn/software/lracluster/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2223-8) contains supplementary material, which is available to authorized users.

High dimensional data clustering is a difficult task due to the curse of dimensionality. Traditional clustering methods usually fail to produce meaningful results for high dimensional data. Hypergraph partition is believed to be a promising method for dealing with this challenge. In this work, a new high dimensional clustering method called Merging Dense SubGraphs (MDSG) is proposed. A graph G is first constructed from the data by defining an adjacency relationship between the data points using Shared k Nearest Neighbors (SNN). Then a hypergraph is created from the graph G by defining the hyperedges to be all the maximal cliques in the graph. After the hypergraph is produced, an improved hypergraph partitioning method is used to produce the final clustering results. The proposed MDSG method is evaluated on several real high dimensional handwritten datasets, and the experimental results show that the proposed method is superior to the traditional clustering method and other hypergraph partition methods for high dimensional handwritten data clustering.

Data clustering groups data so that data which are similar to each other are in the same group and data which are dissimilar to each other are in different groups. Since generally clustering is a subjective activity, it is possible to get different clusterings of the same data depending on the need. This paper attempts to find the best clustering of the data by first carrying out feature selection and using only the selected features, for clustering. A PSO (Particle Swarm Optimization)has been used for clustering but feature selection has also been carried out simultaneously. The performance of the above proposed algorithm is evaluated on some benchmark data sets. The experimental results shows the proposed methodology outperforms the previous approaches such as basic PSO and Kmeans for the clustering problem.

The subject matter of the article is the information technology of thematic segmentation of images of onboard optical-electronic surveillance systems. The goal is the development of information technology for thematic image segmentation of on-board optical-electronic surveillance systems using the system modeling methodology IDEF0. The tasks are: onboard systems of optical-electronic observation images features analysis, requirements for methods formulation , techniques and information technologies of segmentation of optical-electronic images, meta-heuristic methods for solving optimization problems analysis , development of information technology of onboard systems of optical-electronic monitoring images thematic segmentation. The methods used are: methods of probability theory, mathematical statistics, swarm intelligence, data clustering, evolutionary computing, optimization methods, mathematical modeling and digital image processing, analytical and empirical methods of comparative research. The following results were obtained. It has been established that the IDEF0 methodology is based on the SADT structural analysis and design method. In accordance with the syntax and semantics of IDEF0, the information technology of thematic segmentation of optical-electronic images of onboard surveillance systems can be presented in the form of: a tuple, an upper child diagram, and child diagrams. Conclusions. The scientific novelty of the results were obtained as follows: an applied information technology has been developed for image segmentation of on-board optical-electronic observation systems, in which, unlike the known ones, the system modeling methodology IDEF0 is used, which is based on the SADT structural analysis and design method.

Concept Factorization (CF) is a modified version of Nonnegative Matrix Factorization (NMF) and both of them have been proved to be effective matrix factorization methods for dimensionality reduction and data clustering. However, CF is essentially an unsupervised method which cannot utilize any prior knowledge of data. In this paper, we propose a new semi-supervised concept factorization method, called Constrained Concept Factorization with Graph Laplacian (CCF-GL), which not only incorporates the geometrical information of data, but also utilizes the prior label information to enhance the accuracy of CF. Specifically, we expect that the graph laplacian could preserve the intrinsic manifold structure of original data. Meanwhile, in the low-dimensional space, we hope that data points sharing the same label will have the same coordinate, while the coordinates of data points possessing different labels will be as dissimilar as possible. As a result, the learning quality of this semi-supervised CF method has been significantly enhanced. The experimental results on image clustering show good performance of our algorithm.

Peeling off the layers: Skin taping and a novel proteomics approach to study atopic dermatitis