Classical Clustering Methods Research Articles

Understanding the Genome-Wide Transcription Response To Various cAMP Levels in Bacteria Using Phenomenological Models.

Attempts to understand gene regulation by global transcription factors have largely been limited to expression studies under binary conditions of presence and absence of the transcription factor. Studies addressing genome-wide transcriptional responses to changing transcription factor concentration at high resolution are lacking. Here, we create a data set containing the entire Escherichia coli transcriptome in Luria-Bertani (LB) broth as it responds to 10 different cAMP concentrations spanning the biological range. We use the Hill's model to accurately summarize individual gene responses into three intuitively understandable parameters, Emax, n, and k, reflecting the sensitivity, nonlinearity, and midpoint of the dynamic range. Our data show that most cAMP-regulated genes have an n of >2, with their k values centered around the wild-type concentration of cAMP. Additionally, cAMP receptor protein (CRP) affinity to a promoter is correlated with Emax but not k, hinting that a high-affinity CRP promoter need not ensure transcriptional activation at lower cAMP concentrations and instead affects the magnitude of the response. Finally, genes belonging to different functional classes are tuned to have different k, n, and Emax values. We demonstrate that phenomenological models are a better alternative for studying gene expression trends than classical clustering methods, with the phenomenological constants providing greater insights into how genes are tuned in a regulatory network. IMPORTANCE Different genes may follow different trends in response to various transcription factor concentrations. In this study, we ask two questions: (i) what are the trends that different genes follow in response to changing transcription factor concentrations and (ii) what methods can be used to extract information from the gene trends so obtained. We demonstrate a method to analyze transcription factor concentration-dependent genome-wide expression data using phenomenological models. Conventional clustering methods and principal-component analysis (PCA) can be used to summarize trends in data but have limited interpretability. The use of phenomenological models greatly enhances the interpretability and thus utility of conventional clustering. Transformation of dose-response data into phenomenological constants opens up avenues to ask and answer many different kinds of question. We show that the phenomenological constants obtained from the model fits can be used to generate insights about network topology and allows integration of other experimental data such as chromatin immunoprecipitation sequencing (ChIP-seq) to understand the system in greater detail.

Using a Clustering Method to Detect Spatial Events in a Smartphone-Based Crowd-Sourced Database for Environmental Noise Assessment.

Noise has become a very notable source of pollution with major impacts on health, especially in urban areas. To reduce these impacts, proper evaluation of noise is very important, for example by using noise mapping tools. The Noise-Planet project seeks to develop such tools in an open science platform, with a key open-source smartphone tool "NoiseCapture" that allows users to measure and share the noise environment as an alternative to classical methods, such as simulation tools and noise observatories, which have limitations. As an alternative solution, smartphones can be used to create a low-cost network of sensors to collect the necessary data to generate a noise map. Nevertheless, this data may suffer from problems, such as a lack of calibration or a bad location, which lowers its quality. Therefore, quality control is very crucial to enhance the data analysis and the relevance of the noise maps. Most quality control methods require a reference database to train the models. In the context of NC, this reference data can be produced during specifically organized events (NC party), during which contributors are specifically trained to collect measurements. Nevertheless, these data are not sufficient in number to create a big enough reference database, and it is still necessary to complete them. Other communities around the world use NC, and one may want to integrate the data they collected into the learning database. In order to achieve this, one must detect these data within the mass of available data. As these events are generally characterized by a higher density of measurements in space and time, in this paper we propose to apply a classical clustering method, called DBSCAN, to identify them in the NC database. We first tested this method on the existing NC party, then applied it on a global scale. Depending on the DBSCAN parameters, many clusters are thus detected, with different typologies.

Deep learning based conference program organization system from determining articles in session to scheduling

It is very important to create the conference programs correctly in terms of timing and content by preventing problems such as being of articles that do not have a common topic with each other in the same sessions, the parallel of the sessions containing articles on the same topic. It greatly affects the efficiency of conference for participants. Currently, conference programs are organized manually. Considering the conference scope and the number of articles in that conference, it is a difficult and time-consuming process. In this study, an automatic solution to this problem is presented. The use of the SBERT method is provided a more accurate calculation of article similarities compared to baseline methods and is increased the success of other stages. Unlike classical clustering methods, an approach that clusters in such a way that there are equal numbers of data points in the clusters is proposed. In order to find the topic of the clusters determined as sessions, a topic determination approach is proposed that takes into account both keyword and article content similarities. Furthermore, with the proposed approach for session scheduling, the conference program has been planned more effectively by considering the parallel sessions. The ICTAI conference has been chosen to test the proposed approach. The proposed program is compared with both the real program and the programs created using Word2vec and Glove methods. With the proposed program, 10% improvement is achieved in terms of session similarity. In addition, parallel sessions are better planned with no conflicts compared to the real program.

Anomaly Detection of Dam Monitoring Data based on Improved Spectral Clustering

<p>In response to the abnormal data mining in dam safety monitoring, and based on the traditional spectral clustering, this paper presents an anomaly detection method based on improved spectral clustering. This method applies a distance and density adaptive similarity measure. The natural eigenvalue is introduced to adaptively select the neighbors of data points, and the similarity is redefined to be combined with the natural k-nearest neighbor. Furthermore, the shared neighbor is introduced to adjust the similarity between the monitoring data samples according to the regional density. Moreover, considering the distribution of dam monitoring data, the initialization of clustering centers is optimized according to both the density and distance feature. This method can prevent the algorithm from local optimum, better adapt to the density of non-convex dataset, reduce the number of iterations, and enhance the efficiencies of clustering and anomaly detection. Taking the dam slab monitoring data as the research object, experimental datasets are formed. Experiments on these datasets further verify that the method of this paper can effectively adapt to discrete distribution datasets and is superior to the classical spectral clustering method in both clustering and anomaly detection.</p> <p> </p>

An efficient DBSCAN optimized by arithmetic optimization algorithm with opposition-based learning

As unsupervised learning algorithm, clustering algorithm is widely used in data processing field. Density-based spatial clustering of applications with noise algorithm (DBSCAN), as a common unsupervised learning algorithm, can achieve clusters via finding high-density areas separated by low-density areas based on cluster density. Different from other clustering methods, DBSCAN can work well for any shape clusters in the spatial database and can effectively cluster exceptional data. However, in the employment of DBSCAN, the parameters, EPS and MinPts, need to be preset for different clustering object, which greatly influences the performance of the DBSCAN. To achieve automatic optimization of parameters and improve the performance of DBSCAN, we proposed an improved DBSCAN optimized by arithmetic optimization algorithm (AOA) with opposition-based learning (OBL) named OBLAOA-DBSCAN. In details, the reverse search capability of OBL is added to AOA for obtaining proper parameters for DBSCAN, to achieve adaptive parameter optimization. In addition, our proposed OBLAOA optimizer is compared with standard AOA and several latest meta heuristic algorithms based on 8 benchmark functions from CEC2021, which validates the exploration improvement of OBL. To validate the clustering performance of the OBLAOA-DBSCAN, 5 classical clustering methods with 10 real datasets are chosen as the compare models according to the computational cost and accuracy. Based on the experimental results, we can obtain two conclusions: (1) the proposed OBLAOA-DBSCAN can provide highly accurately clusters more efficiently; and (2) the OBLAOA can significantly improve the exploration ability, which can provide better optimal parameters.

A multi-view clustering framework via integrating K-means and graph-cut

Graph-cut and K-means are two classical clustering methods, which are used by most of existing clustering methods. First, a multi-view clustering framework via K-means and graph is proposed and a efficient clustering method is developed based on this framework. Our method calculates the discrete cluster assignment matrix directly and includes the auto-weighted strategy to consider the different contributions of views. Additionally, by generating the distance matrices for views with the corresponding graphs, our method fits both Gaussian and non-Gaussian distributed datasets. Moreover, the proposed algorithm is able to obtain robust results because it does not need initializing and computing cluster centroids. Extensive experiments on a synthetic dataset and four real-world datasets indicate the effectiveness of our method.

Air Traffic Trajectory Operation Mode Mining Based on Clustering

When processing track sequences, it is time-consuming and difficult to separate clusters with substantial density variations to deal with the problem of classic clustering methods mining common flight patterns in airspace. To overcome these issues, this research proposes a clustering-based technique for mining air traffic trajectory operation patterns. The track data are first decoded and rebuilt using a motion-based track training approach; next, a compression based on a deep autoencoder (OFAE) is provided, allowing the model to deal with the high-dimensional trajectory vector containing derived information. The compressed trajectory data are made as compact and dense as feasible using the L21 norm constraint, which reduces the operation time and improves the performance of the clustering process. The compressed trajectory is then analyzed using a fast-clustering algorithm based on density peaks (DPCA). To save time, a more refined distance measurement technique is added into the model in order to achieve the usual aircraft operation mode in the terminal area. The accuracy of trajectory prediction can be improved by using the generated unitized and high-class similarity trajectory data.

A two-echelon fuzzy clustering based heuristic for large-scale bike sharing repositioning problem

This paper considers the large-scale bike sharing repositioning problem (BSRP) frequently encountered in modern bike sharing systems. To cope with customer demand fluctuations, BSRP aims to identify the optimal routes traveled by homogeneous vehicles to fulfill the inventory needs at each bike-sharing station in order to minimize the total cost. It is computationally intractable to obtain promising solutions, especially for large-scale instances, given its NP-hardness. This paper adapts the two-echelon structure from the vehicle routing problem (VRP) to BSRP, proposes the two-echelon BSRP model and demonstrates its competitiveness. First, a novel fuzzy clustering strategy quantitatively considering the correlation between stations is designed to construct the clusters with satellites and their corresponding customers to form the two-echelon structure. Then, a tailored fuzzy correlation based adaptive variable neighborhood search (FC-AVNS) with newly designed neighborhood structures and several feasibility and satisfaction check mechanisms is proposed to construct the routes within and between the clusters. Performance of the proposed method is compared with that of a exact model solved by CPLEX and other three state-of-the-art methods. Also, comparisons are made between the presented fuzzy clustering strategy and the other two classical clustering methods taken from the literature. Computational experiments based on medium- and large-scale instances involving 100 to 519 stations are performed and the results validate the superior performance of the proposed method with respect to solution efficiency and stability.

РЕШЕНИЕ ЗАДАЧИ ИНТЕЛЛЕКТУАЛЬНОГО АНАЛИЗА ДАННЫХ НА ОСНОВЕ БИОИНСПИРИРОВАННОГО АЛГОРИТМА

The article discusses a bioinspired algorithm for solving the problems of intellectual analysis.The integration of bioinspired algorithms for solving data mining problems is a promisingarea of research. As a bioinspired algorithm, an algorithm based on the adaptive behavior of anant colony is considered. The ant colony algorithm allows for a high-quality search for promisingsolutions to obtain optimal and quasi-optimal solutions. The algorithm has the ability to search forsuitable logical conditions. The ant colony algorithm is based on the example of the behavior ofliving ants in nature. Ants are able to find the shortest solution by adapting to changes in the environment.The authors proposed a modified ant colony algorithm for solving the problem of datamining. The clustering problem was chosen as the task of data mining. Clustering is a combiningof similar objects into groups, is one of the fundamental tasks in the field of data analysis andData Mining. The list of application areas where it is applied is wide: image segmentation, marketing,anti-fraud, forecasting, text analysis and many others. The solution to this problem is of particular relevance in the context of the constantly growing volume of generated, transmitted andprocessed data. Classical clustering methods are optimized by combining with the proposedbioinspired optimization algorithm - the ant algorithm. The proposed method is a model in whichants are represented as agents that randomly move in the solution space with some restrictions(for example, obstacles in their path). To determine the effectiveness of the developed modified antalgorithm (ALA) with the clustering algorithm, the authors carried out a series of computationalexperiments. For comparison, we took the genetic algorithm, the monkey algorithm and the wolfalgorithm. The simulation results prove that the clustering-based ant algorithm gives better resultsthan other proposed algorithms.

DeLUCS: Deep learning for unsupervised clustering of DNA sequences.

We present a novel Deep Learning method for the Unsupervised Clustering of DNA Sequences (DeLUCS) that does not require sequence alignment, sequence homology, or (taxonomic) identifiers. DeLUCS uses Frequency Chaos Game Representations (FCGR) of primary DNA sequences, and generates “mimic” sequence FCGRs to self-learn data patterns (genomic signatures) through the optimization of multiple neural networks. A majority voting scheme is then used to determine the final cluster assignment for each sequence. The clusters learned by DeLUCS match true taxonomic groups for large and diverse datasets, with accuracies ranging from 77% to 100%: 2,500 complete vertebrate mitochondrial genomes, at taxonomic levels from sub-phylum to genera; 3,200 randomly selected 400 kbp-long bacterial genome segments, into clusters corresponding to bacterial families; three viral genome and gene datasets, averaging 1,300 sequences each, into clusters corresponding to virus subtypes. DeLUCS significantly outperforms two classic clustering methods (K-means++ and Gaussian Mixture Models) for unlabelled data, by as much as 47%. DeLUCS is highly effective, it is able to cluster datasets of unlabelled primary DNA sequences totalling over 1 billion bp of data, and it bypasses common limitations to classification resulting from the lack of sequence homology, variation in sequence length, and the absence or instability of sequence annotations and taxonomic identifiers. Thus, DeLUCS offers fast and accurate DNA sequence clustering for previously intractable datasets.

Discovering cell types using manifold learning and enhanced visualization of single-cell RNA-Seq data

Identifying relevant disease modules such as target cell types is a significant step for studying diseases. High-throughput single-cell RNA-Seq (scRNA-seq) technologies have advanced in recent years, enabling researchers to investigate cells individually and understand their biological mechanisms. Computational techniques such as clustering, are the most suitable approach in scRNA-seq data analysis when the cell types have not been well-characterized. These techniques can be used to identify a group of genes that belong to a specific cell type based on their similar gene expression patterns. However, due to the sparsity and high-dimensionality of scRNA-seq data, classical clustering methods are not efficient. Therefore, the use of non-linear dimensionality reduction techniques to improve clustering results is crucial. We introduce a method that is used to identify representative clusters of different cell types by combining non-linear dimensionality reduction techniques and clustering algorithms. We assess the impact of different dimensionality reduction techniques combined with the clustering of thirteen publicly available scRNA-seq datasets of different tissues, sizes, and technologies. We further performed gene set enrichment analysis to evaluate the proposed method’s performance. As such, our results show that modified locally linear embedding combined with independent component analysis yields overall the best performance relative to the existing unsupervised methods across different datasets.

Fast Multi-view Clustering via Prototype Graph

Multi-view clustering attracts considerable attention due to its effectiveness in unsupervised learning. However, previous multi-view spectral clustering methods include two separated steps: 1) Obtaining a spectral embedding; 2) Performing classical clustering methods. Although these methods have achieved promising performance, there is still some limitations. First, in computing spectral embedding, multi-view spectral clustering approaches exist high computational complexity since they usually need eigenvalue decomposition on laplacian matrix L. Its computational complexity is O(n³) where n is the number of samples; Second, in constructing similarity matrices, previous methods need to compute similarity between any two samples; Third, the two-stage approach only can obtain sub-optimal solution; Fourth, treating equally all views is unreasonable. To address these issues, we propose a Fast Multi-view Clustering via Prototype Graph (FMVPG) method. Specifically, the prototype graph is firstly constructed, and then simultaneously perform spectral embedding to obtain the real matrix and spectral rotation to get the indicator matrix. In addition, the Alternating Direction Method of Multipliers (ADMM) is used to solve the joint optimization problem. Further, we conduct extensive experiments to evaluate the proposed FMVPG approach. These experimental results show the comparable or even better clustering performance than the state-of-the-art approaches.

EdClust: A heuristic sequence clustering method with higher sensitivity.

The development of high-throughput technologies has produced increasing amounts of sequence data and an increasing need for efficient clustering algorithms that can process massive volumes of sequencing data for downstream analysis. Heuristic clustering methods are widely applied for sequence clustering because of their low computational complexity. Although numerous heuristic clustering methods have been developed, they suffer from two limitations: overestimation of inferred clusters and low clustering sensitivity. To address these issues, we present a new sequence clustering method (edClust) based on Edlib, a C/C[Formula: see text] library for fast, exact semi-global sequence alignment to group similar sequences. The new method edClust was tested on three large-scale sequence databases, and we compared edClust to several classic heuristic clustering methods, such as UCLUST, CD-HIT, and VSEARCH. Evaluations based on the metrics of cluster number and seed sensitivity (SS) demonstrate that edClust can produce fewer clusters than other methods and that its SS is higher than that of other methods. The source codes of edClust are available from https://github.com/zhang134/EdClust.git under the GNU GPL license.

Fair hierarchical clustering of substations based on Gini coefficient

For the load modeling of a large power grid, the large number of substations covered by it must be segregated into several categories and, thereafter, a load model built for each type. To address the problem of skewed clustering tree in the classical hierarchical clustering method used for categorizing substations, a fair hierarchical clustering method is proposed in this paper. First, the fairness index is defined based on the Gini coefficient. Thereafter, a hierarchical clustering method is proposed based on the fairness index. Finally, the clustering results are evaluated using the contour coefficient and the t-SNE two-dimensional plane map. The substations clustering example of a real large power grid considered in this paper illustrates that the proposed fair hierarchical clustering method can effectively address the problem of the skewed clustering tree with high accuracy.

ModelSet: a dataset for machine learning in model-driven engineering

The application of machine learning (ML) algorithms to address problems related to model-driven engineering (MDE) is currently hindered by the lack of curated datasets of software models. There are several reasons for this, including the lack of large collections of good quality models, the difficulty to label models due to the required domain expertise, and the relative immaturity of the application of ML to MDE. In this work, we present ModelSet, a labelled dataset of software models intended to enable the application of ML to address software modelling problems. To create it we have devised a method designed to facilitate the exploration and labelling of model datasets by interactively grouping similar models using off-the-shelf technologies like a search engine. We have built an Eclipse plug-in to support the labelling process, which we have used to label 5,466 Ecore meta-models and 5,120 UML models with its category as the main label plus additional secondary labels of interest. We have evaluated the ability of our labelling method to create meaningful groups of models in order to speed up the process, improving the effectiveness of classical clustering methods. We showcase the usefulness of the dataset by applying it in a real scenario: enhancing the MAR search engine. We use ModelSet to train models able to infer useful metadata to navigate search results. The dataset and the tooling are available at https://figshare.com/s/5a6c02fa8ed20782935c and a live version at http://modelset.github.io.

QuickDSC: Clustering by Quick Density Subgraph Estimation

Density-based clustering is a traditional research topic with the capability of determining clusters of arbitrary shapes. Besides, through the Density Estimator (DE), density-based methods such as MeanShift, and QuickShift can find the local density maximums as Modes that are excellent representatives of the clusters. However, concentrating on the modes only may suffer from the over-segmentation problem. On the other hand, most density-based methods cannot satisfy the scenario requiring partitioning the data samples into exactly K clusters. To overcome these issues, QuickDSC: a novel and efficient clustering algorithm that groups the samples through the Quick Density Subgraph Estimation, is proposed in this work. It firstly identifies the high-density-connected samples as the Density Subgraphs (DSs). And then, the importance of DSs is estimated from two aspects: density and geometric weight. The top-K important DSs are selected as the cluster centers and based on which the cluster memberships of remaining samples are determined.QuickDSC incorporates three crucial clustering attributes: (1) the cluster centroids are modes (as in density-based methods); (2) able to efficiently return results by utilizing the underlying density structure (as in hierarchical clustering methods); and (3) it explicitly returns K clusters (e.g., K-Means, K-Modes). In addition, QuickDSC is theoretically and empirically efficient. It is only slightly slower than classical clustering methods such as K-Means and DBSCAN. Experiments on artificial and real-world datasets demonstrate the advantages of the proposed method, and the clustering quality outperforms the state-of-the-art approaches.

Network Public Opinion Monitoring Based on MapReduce

Public opinion information on the network is abundantly decentralized in Internet, these false ones, easily misleading, not legally binding, it is difficult to deal with. Quickly master information is a prerequisite to deal with the network of public opinion, clustering algorithm in data mining plays an important role in in the collection of information statistics. Disadvantages of classical clustering methods are high resources consumption, low efficiency, high time and space complexity, and difficult to deal with large-scale data processing in massive network. To the network public opinion text as the research object, in-depth study of summarizing the network public opinion monitoring technology based on distributed MapReduce, including parallel technology, improved clustering algorithm, relational database and distributed database construction, which is in order to improve the efficiency of information processing, to reduce the pin.

Robust clustering with sparse corruption via [formula omitted], [formula omitted] norm constraint and Laplacian regularization

Clustering has been applied in machine learning, data mining and so on, and has received extensive attention. However, since some data has noise or outliers, these noise or outliers easily bring about the objective function with large errors. In this paper, a robust clustering model with ℓ2,1, ℓ1 norm and Laplacian regularization (RCLR) is proposed, on which, sparse error matrix is introduced to express sparse noise, and ℓ1 norm is introduced to alleviate the sparse noise. In addition, the ℓ2,1 norm is also introduced to achieves space robust by virtue of its nice rotation invariance property. Therefore, our RCLR is insensitive to data noise and outliers. More importantly, the Laplacian regularization is introduced into the RCLR to improve the clustering accuracy. In order to solve the optimization objective of clustering problem, we propose an iterative updating algorithm, named alternating direction method of multipliers (ADMM), to update each optimization variable alternatively, and the convergence of the proposed algorithm is also proved in theory. Finally, experimental results on a total of eleven datasets of three types of datasets, elaborate the superiority of this method over six existing classical clustering methods. Three types of datasets include face images dataset, handwritten recognition dataset, and UCI dataset. In particular, our RCLR clustering approach has the best effect on face image dataset.

Nonparametric homogeneity pursuit in functional-coefficient models

ABSTRACT This paper explores the homogeneity of coefficient functions in nonlinear models with functional coefficients and identifies the underlying semiparametric modelling structure. With initial kernel estimates, we combine the classic hierarchical clustering method with a generalised version of the information criterion to estimate the number of clusters, each of which has a common functional coefficient, and determine the membership of each cluster. To identify a possible semi-varying coefficient modelling framework, we further introduce a penalised local least squares method to determine zero coefficients, non-zero constant coefficients and functional coefficients which vary with an index variable. Through the nonparametric kernel-based cluster analysis and the penalised approach, we can substantially reduce the number of unknown parametric and nonparametric components in the models, thereby achieving the aim of dimension reduction. Under some regularity conditions, we establish the asymptotic properties for the proposed methods including the consistency of the homogeneity pursuit. Numerical studies, including Monte-Carlo experiments and two empirical applications, are given to demonstrate the finite-sample performance of our methods.

MODELLING SEMANTIC KERNEL OF WEB RESOURCE

The article presents an attempt to describe mathematically the effect of the semantic kernel of a web resource on the Internet. In accordance with the theory of marketing, the product that we want to sell on the network is characterized by the following basic properties: price, time and place. In other words, a potential buyer wants to receive a given product in the right place at a given time. To satisfy this need, it is necessary to use the classic component of marketing, product promotion. However, this component is now becoming a fully virtual instrument. This tool functions in a hypertext, video and image environment. Therefore, the user analyzes the meaning of these elements in order to get the desired product. The results of web projects carried out in this area indicate the emergence of a new phenomenon, which reflects the main meaning of virtual promotion – this is the semantic core. The core is a short annotation of the main properties of the product, its location and time of appearance. Therefore, the purpose of this article is both a presentation of a new object of research and a mathematical description. It is assumed that the semantic core is formed on the basis of natural language terms. In other words, the semantic core is a set of keywords that are grouped by meaning. We propose to use data mining approaches for clustering to group terms. The classic clustering method at the moment is k-means. The article presents a model of the semantic core based on this method. This method and its distance function are considered as the second stage of web content processing. At the first stage, web content is converted into a semantic web. However, the k-means technique has significant drawbacks when modeling the semantic core. Therefore, in the development of this idea, the work shows an alternative way to modeling the kernel. As an alternative approach, the construction of clusters based on the concept of maximum flow is considered. This approach has the significant advantage that the type of links in the semantic network overlaps with the type of distance function in this method. As a result, on a real web project, the effect of the connection between the semantic core model and the level of new users of the web resource was demonstrated over the past five years.
 Keywords: semantic kernel, keyword, k-means, max flow.