Data Clustering Algorithm Research Articles

Multidimensional heterogeneous data clustering algorithm for power transmission and transformation equipment

Considering that the operation of power transmission and transformation equipment is not timely discovered due to the untimely data integration, a multi-dimensional heterogeneous data clustering algorithm for power transmission and transformation equipment based on multimodal deep learning is proposed. The multi-modal deep learning method is used to mine relevant data and measure the similarity between the data, which can improve the accuracy of subsequent multi-bit heterogeneous data clustering of power transmission and transformation equipment. Set up a clustering center and process data clustering to complete multi-dimensional heterogeneous data clustering of power transmission and transformation equipment. The experimental results show that the method has high clustering accuracy in the clustering of voltage deviation overrun times, voltage harmonic total distortion rate overrun times, and voltage flicker overrun times.

Data-Oriented Intelligent Judgment Platform for Red Tourism Industry in Northeast Guangdong Based on Tourism Big Data Clustering Algorithm

Defining big data in the context of tourism forecasting, summarizes the changes it brings to tourism business decision-making. The experimental part deals with the tourism metadata shared by the website, and applies a suitable clustering algorithm to generate the density pattern of the most frequently visited places by tourists. At the same time, it proposes a method to obtain local core categories through the discovery of maximal cliques, and proposes a parallelization of the maximal clique discovery algorithm. strategy, and then propose a parallel strategy for the entire algorithm and experiment on real datasets. Realize the intelligence, intelligence, and semantics of the video surveillance network, so that the police can be freed from the labor of watching video surveillance. Through the extraction and correlation of data feature attributes, the collision can obtain "object laws", so as to make efficient and accurate decisions.

Crude oil production prediction based on an intelligent hybrid modelling structure generated by using the clustering algorithm in big data

Since the behavior of a complex dynamic system for a large oil field in Iraq is significantly influenced by many nonlinearities, its dependent parameters exhibit non-stationary with a very high delay time. Developing white-box modelling approaches for such dynamic oil well production cannot handle these large data sets with all dependent dimensions and their non-linear effects. Therefore, this study adopts the hybrid model that combines white-box and black-box to address such problems because the model outputs require various variable types to achieve optimal fitness to measured values. The hybrid model structure needs to evolve with changes in the physical parameters (white-box part) and Neural Networks' Weights (black-box part). The model structure of the proposed hybrid network relied on converting fuzzy rules in a Takagi–Sugeno–Kang Fuzzy System (TSK-FS) into a multilayer perceptron network (MLP). The hybrid parameters are formulated concerning six-dimensional dependent variables to describe them in matrix form or layer and by which can quantify total model outputs. After mapping categorical variables to tuples of MLP, the Gauss-Newton regression (GNR) provides an optimal update of the hybrid parameters to get the best fitting of the model outputs with the target of the dataset. The clustering technique and GNR promote predictive performance due to reducing uncertainties in the hybrid parameters. Due to time being the most effective of the independent variables for predicting oil production, datasets are classified into different clusters based on time. The actual field dataset for training and validation is collected from Zubair Oil Field (9 oil wells), which is implemented to build the proposed model. The results of the hybrid model indicate that the development of the proposed structure has achieved the high capability to represent such big data which is the most imperative feature of the proposed model. Furthermore, obtained results show its accuracy far outpacing competitors and achieving a significant improvement in predictive performance.

Establishment of a Landscape Information Model (LIM) and AI Convergence Plan through the 3D Digital Transformation of Railway Surroundings

Digital transformation projects have been undertaken in the land transportation and railway industries, including the introduction of various smart construction technologies. With the expansion of policies to increase the share of railway transportation as an environmentally sustainable means of transportation that meets the needs of the carbon-neutral era, 3D digital information is required throughout the entire chain of railway construction, route selection, status analysis, design, construction, and maintenance. The need for scientific and rational decision making is increasing. In this study, based on point cloud data acquired by an unmanned aerial vehicle (UAV) and a handheld mobile device, the landscape infrastructure around a railway was digitally converted, and a railway Landscape Information Model (LIM) process that modeled various types of landscape information was derived. Additionally, through the voxelization of 3D data, information regarding a railway’s surrounding environment, analyzed as a 3D volume concept and a convergence plan with deep-learning-based artificial intelligence (AI) technology, was presented through object recognition using a clustering algorithm. A railway LIM dataset could be created from a total of seven major categories, and massive data processing through AI convergence will be a future possibility through optimization of the point cloud data clustering algorithm. The future of the railway industry requires the establishment of a railway LIM for the integrated management of a railway’s surrounding environment and building information modeling (BIM) of structures such as tunnels. The railway LIM process has potential for use in various fields, such as environmental management and safety improvement for disaster prevention.

PARMA-CC: A family of parallel multiphase approximate cluster combining algorithms

Clustering is a common task in data analysis applications. Despite the extensive literature, the continuously increasing volumes of data produced by sensors (e.g., rates of several MB/s by 3D scanners such as LIDAR sensors), and the time-sensitivity of the applications leveraging the clustering outcomes (e.g., detecting critical situations such as detecting boundary crossing from a robot arm that could injure human beings) demand for efficient data clustering algorithms that can effectively utilize the increasing computational capacities of modern hardware. To that end, we leverage approximation and parallelization, where the former is to scale down the amount of data, and the latter is to scale up the computation. Regarding parallelization, we explore a design space for synchronization and workload distribution among the threads. As we study different parts of the design space, we propose representative Parallel Multiphase Approximate Cluster Combining, abbreviated as PARMA-CC, algorithms.We show that PARMA-CC algorithms yield equivalent clustering outcomes despite their different approaches. Furthermore, we show that certain PARMA-CC algorithms can achieve higher efficiency with respect to certain properties of the data to be clustered. Generally speaking, in PARMA-CC algorithms, parallel threads compute summaries associated with clusters of data (sub)sets. As the threads concurrently combine the summaries, they construct a comprehensive summary of the sets of clusters. By approximating a cluster with its respective geometrical summaries, PARMA-CC algorithms scale well with increasing data volumes, and, by computing and efficiently combining the summaries in parallel, they enable latency improvements. PARMA-CC algorithms utilize special data structures that enable parallelism through in-place data processing. As we show in our analysis and evaluation, PARMA-CC algorithms can complement and outperform well-established methods, with significantly better timeliness especially when utilizing multiple threads, while still providing highly accurate results in a variety of data sets, even with skewed data distributions, which cause the traditional approaches to exhibit their worst-case behaviour.

Multivariate semiparametric control charts for mixed-type data.

A useful tool that has gained popularity in the Quality Control area is the control chart which monitors a process over time, identifies potential changes, understands variations, and eventually improves the quality and performance of the process. This article introduces a new class of multivariate semiparametric control charts for monitoring multivariate mixed-type data, which comprise both continuous and discrete random variables (rvs). Our methodology leverages ideas from clustering and Statistical Process Control to develop control charts for MIxed-type data. We propose four control chart schemes based on modified versions of the KAy-means for MIxed LArge KAMILA data clustering algorithm, where we assume that the two existing clusters represent the reference and the test sample. The charts are semiparametric, the continuous rvs follow a distribution that belongs in the class of elliptical distributions. Categorical scale rvs follow a multinomial distribution. We present the algorithmic procedures and study the characteristics of the new control charts. The performance of the proposed schemes is evaluated on the basis of the False Alarm Rate and in-control Average Run Length. Finally, we demonstrate the effectiveness and applicability of our proposed methods utilizing real-world data.

Clustering and Mining Algorithm of Factor Structure Data of Physical Education Teachers' Teaching Behavior Based on Internet Information Retrieval Algorithm

Integrating visual word list and Rocchio algorithm, constructs an implicit relevance feedback retrieval model in semantic space, and judges information retrieval preference. User demand mining is introduced, Jensen-Shannon divergence is used to calculate the relative entropy distance between the probability distributions of document sets, and similarity matching is calculated to complete interactive information retrieval. Teaching ability is gradually formed by teachers in long-term teaching practice, and is stably and comprehensively reflected in teaching viewpoints, teaching methods, teaching skills, teaching style and so on. The weight analysis of each index in the evaluation index system was carried out by using the analytic hierarchy process, and the selection of the evaluation subject was completed through the third round of expert questionnaires, and the evaluation criteria were formulated in combination with the opinions of tutors and some expert teachers.

Consistency-Induced Multiview Subspace Clustering.

Multiview clustering has received great attention and numerous subspace clustering algorithms for multiview data have been presented. However, most of these algorithms do not effectively handle high-dimensional data and fail to exploit consistency for the number of the connected components in similarity matrices for different views. In this article, we propose a novel consistency-induced multiview subspace clustering (CiMSC) to tackle these issues, which is mainly composed of structural consistency (SC) and sample assignment consistency (SAC). To be specific, SC aims to learn a similarity matrix for each single view wherein the number of connected components equals to the cluster number of the dataset. SAC aims to minimize the discrepancy for the number of connected components in similarity matrices from different views based on the SAC assumption, that is, different views should produce the same number of connected components in similarity matrices. CiMSC also formulates cluster indicator matrices for different views, and shared similarity matrices simultaneously in an optimization framework. Since each column of similarity matrix can be used as a new representation of the data point, CiMSC can learn an effective subspace representation for the high-dimensional data, which is encoded into the latent representation by reconstruction in a nonlinear manner. We employ an alternating optimization scheme to solve the optimization problem. Experiments validate the advantage of CiMSC over 12 state-of-the-art multiview clustering approaches, for example, the accuracy of CiMSC is 98.06% on the BBCSport dataset.

VoomSOM: voom-based Self-Organizing Maps for Clustering RNASequencing Data

Background: Due to overdispersion in the RNA-Seq data and its discrete structure, clustering samples based on gene expression profiles remains a challenging problem, and several clustering approaches have been developed so far. However, there is no “gold standard” strategy for clustering RNA-Seq data, so alternative approaches are needed. Objective: In this study, we presented a new clustering approach, which incorporates two powerful methods, i.e., voom and self-organizing maps, into the frequently used clustering algorithms such as kmeans, k-medoid and hierarchical clustering algorithms for RNA-seq data clustering. Methods: We first filter and normalize the raw RNA-seq count data. Then to transform counts into continuous data, we apply the voom method, which outputs the log-cpm matrix and sample quality weights. After the voom transformation, we apply the SOM algorithm to log-cpm values to get the codebook used in the downstream analysis. Next, we calculate the weighted distance matrices using the sample quality weights obtained from voom transformation and codebooks from the SOM algorithm. Finally, we apply k-means, k-medoid and hierarchical clustering algorithms to cluster samples. Results: The performances of the presented approach and existing methods are compared over simulated and real datasets. The results show that the new clustering approach performs similarly or better than other methods in the Rand index and adjusted Rand index. Since the voom method accurately models the observed mean-variance relationship of RNA-seq data and SOM is an efficient algorithm for modeling high dimensional data, integrating these two powerful methods into clustering algorithms increases the performance of clustering algorithms in overdispersed RNA-seq data. Conclusion: The proposed algorithm, voomSOM, is an efficient and novel clustering approach that can be applied to RNA-Seq data clustering problems.

K-Anonymity Privacy Protection Algorithm for Multi-Dimensional Data against Skewness and Similarity Attacks.

Currently, a significant focus has been established on the privacy protection of multi-dimensional data publishing in various application scenarios, such as scientific research and policy-making. The K-anonymity mechanism based on clustering is the main method of shared-data desensitization, but it will cause problems of inconsistent clustering results and low clustering accuracy. It also cannot defend against several common attacks, such as skewness and similarity attacks at the same time. To defend against these attacks, we propose a K-anonymity privacy protection algorithm for multi-dimensional data against skewness and similarity attacks (KAPP) combined with t-closeness. Firstly, we propose a multi-dimensional sensitive data clustering algorithm based on improved African vultures optimization. More specifically, we improve the initialization, fitness calculation, and solution update strategy of the clustering center. The improved African vultures optimization can provide the optimal solution with various dimensions and achieve highly accurate clustering of the multi-dimensional dataset based on multiple sensitive attributes. It ensures that multi-dimensional data of different clusters are different in sensitive data. After the dataset anonymization, similar sensitive data of the same equivalence class will become less, and it eventually does not satisfy the premise of being theft by skewness and similarity attacks. We also propose an equivalence class partition method based on the sensitive data distribution difference value measurement and t-closeness. Namely, we calculate the sensitive data distribution's difference value of each equivalence class and then combine the equivalence classes with larger difference values. Each equivalence class satisfies t-closeness. This method can ensure that multi-dimensional data of the same equivalence class are different in multiple sensitive attributes, and thus can effectively defend against skewness and similarity attacks. Moreover, we generalize sensitive attributes with significant weight and all quasi-identifier attributes to achieve anonymous protection of the dataset. The experimental results show that KAPP improves clustering accuracy, diversity, and anonymity compared to other similar methods under skewness and similarity attacks.

Student Psychology based optimized routing algorithm for big data clustering in IoT with MapReduce framework

Clustering plays a fundamental task in the process of data mining, which remains more demanding due to the ever-increasing dimension of accessible datasets. Big data is considered more populous as it has the ability to handle various sources and formats of data under numerous highly developed technologies. This paper devises a robust and effective optimization-based Internet of Things (IoT) routing technique, named Student Psychology Based Optimization (SPBO) -based routing for the big data clustering. When the routing phase is done, big data clustering is carried out using the Deep Fractional Calculus-Improved Invasive Weed Optimization fuzzy clustering (Deep FC-IIWO fuzzy clustering) approach. Here, the Mapreduce framework is used to minimizing the over fitting issues during big data clustering. The process of feature selection is performed in the mapper phase in order to select the major features using Minkowski distance, whereas the clustering procedure is carried out in the reducer phase by Deep FC-IIWO fuzzy clustering, where the FC-IIWO technique is designed by the hybridization of Improved Invasive Weed Optimizer (IIWO) and Fractional Calculus (FC). The developed SPBO-based routing approach achieved effective performance in terms of energy, clustering accuracy, jaccard coefficient, rand coefficient, computational time and space complexity of 0.605 J, 0.935, 0.947, 0.954, 2100.6 s and 72KB respectively.

A Novel Hybrid High-Dimensional PSO Clustering Algorithm Based on the Cloud Model and Entropy

With the increase in the number of high-dimensional data, the characteristic phenomenon of unbalanced distribution is increasingly presented in various big data applications. At the same time, most of the existing clustering and feature selection algorithms are based on maximizing the clustering accuracy. In addition, the hybrid approach can effectively solve the clustering problem of unbalanced data. Aiming at the shortcomings of the unbalanced data clustering algorithm, a hybrid high-dimensional multi-objective PSO clustering algorithm is proposed based on the cloud model and entropy (HHCE-MOPSO). Furthermore, the feasibility of the hybrid PSO is verified by the simulation of the multi-objective test function. The results not only broaden the new theory and method of clustering algorithm for unbalanced data, but also verify the accuracy and feasibility of the hybrid PSO. Furthermore, the clustering analysis method based on information entropy is a new method. As a result, the research results have both important scientific value and good practical significance.

Study on Library Management System Based on Data Mining and Clustering Algorithm

In order to improve the information retrieval and resource sharing abilities of the library and establish an intelligent library information management system, alibrary management system based on data mining and clustering algorithm is proposed. The system development is divided into two modules: library mining algorithm design and information management system software development. Library data mining design and clustering algorithm of library management system development. System development is divided into two modules: library mining algorithm design and information management system software development. Big data fusion and feature clustering methods is used to design intelligent information retrieval algorithms in library information management to establish library management database. Realizing the development and design of library management system under embedded environment, and finally experimental test analysis is carried out. Results show that the use of the proposed method for library information management can improve the accuracy of book recommendation, which is 16.7% higher than traditional methods on average. It has good data mining, and has good performance indicators in data recall and precision. Therefore, this algorithm is not only a means of automated management of library systems, but also an effective means to realize library information modernization.

Opposition Learning Based Improved Bee Colony Optimization (OLIBCO) Algorithm for Data Clustering

Opposition Learning Based Improved Bee Colony Optimization (OLIBCO) Algorithm for Data Clustering

Network-Based Structural Learning Nonnegative Matrix Factorization Algorithm for Clustering of scRNA-Seq Data.

Single-cell RNA sequencing (scRNA-seq) measures expression profiles at the single-cell level, which sheds light on revealing the heterogeneity and functional diversity among cell populations. The vast majority of current algorithms identify cell types by directly clustering transcriptional profiles, which ignore indirect relations among cells, resulting in an undesirable performance on cell type discovery and trajectory inference. Therefore, there is a critical need for inferring cell types and trajectories by exploiting the interactions among cells. In this study, we propose a network-based structural learning nonnegative matrix factorization algorithm (aka SLNMF) for the identification of cell types in scRNA-seq, which is transformed into a constrained optimization problem. SLNMF first constructs the similarity network for cells and then extracts latent features of the cells by exploiting the topological structure of the cell-cell network. To improve the clustering performance, the structural constraint is imposed on the model to learn the latent features of cells by preserving the structural information of the networks, thereby significantly improving the performance of algorithms. Finally, we track the trajectory of cells by exploring the relationships among cell types. Fourteen scRNA-seq datasets are adopted to validate the performance of algorithms with the number of single cells varying from 49 to 26,484. The experimental results demonstrate that SLNMF significantly outperforms fifteen state-of-the-art methods with 15.32% improvement in terms of accuracy, and it accurately identifies the trajectories of cells. The proposed model and methods provide an effective strategy to analyze scRNA-seq data. (The software is coded using matlab, and is freely available for academic https://github.com/xkmaxidian/SLNMF).

DyClee-N&C: a clustering algorithm for heterogeneous data based situation assessment

In data-based situation assessment applications, the proliferation of data acquired and recorded on current technological systems is a key issue in that data remain unlabeled because labeling would require too much time and implies prohibitive costs. The data should therefore speak for itself. The different situations, e.g., normal or faulty, must hence be learned only from the data. Clustering methods, also named unsupervised classification methods, can be used for that purpose. These methods are designed to cluster the samples according to some similarity criterion. The different clusters can be associated to different situations whose discrimination may be relevant to obtain a proper diagnosis.Numerous algorithms have been developed in recent years for clustering numeric data but these methods are not applicable to categorical data. This is the case of the algorithm DyClee, named DyClee-N in the paper. However, in many application domains, qualitative features are key to properly describe the different situations. DyClee-N was recast to produce a version, named DyClee-C that accepts categorical features, but only categorical features. This paper presents DyClee-N&C that subsumes both the numeric and categorical feature based algorithms DyClee-N and DyClee-C respectively. DyClee-N&C is applied to a data set of the literature for the evaluation of risk in the automobile domain and compared to state of the art clustering methods.

The similarity of the similarity measures in the context of clustering algorithms for categorical data

This research aims to assess the similarity between determining the value of the similarity of objects by using various measures. Particularly for categorical data, the used similarity measures may behave differently depending on whether the analyzed data are binary, non-binary, or mixed. In the case of categorical data, it begins to matter how many attributes we have and how many different values they can take. Cluster analysis is an essential technique in unsupervised machine learning. Object-clustering cannot be determined without determining similarities between objects in the set and searching for clusters with the most significant internal consistency and external separatory. Therefore, we need knowledge about which measure behaves similarly and which differs depending on the nature of the analyzed data. According to our knowledge, there is no such research in the literature. In research, we analyzed six different measures for eight different datasets (differing in size and data type). The results say, among others, which measures take the longest time for calculations and which are the most correlated with others, so they can be used interchangeably without fear of losing valuable information about the mutual similarity of objects.

Parallel progressive-based inductive subspace and fuzzy-based firefly algorithm for high ensemble data clustering

Recently, the framework is proposed with techniques such as random subspace and constraint propagation for handling high dimensional data ensemble clustering. Huge dataset clustering is difficult for conventional sequential clustering methods since it requires higher computation time. Distributed parallel processing and methods are consequently useful towards attaining results and scalability constraints of clustering huge datasets. So, in this work, the parallel progressive-based inductive subspace ensemble clustering (PPISEC) algorithm is introduced with the concept of MapReduce (MR) to perform high dimensional data clustering. Depending on micro-clusters and correspondence relative, the clustering method is designed which is easily parallelised via MR and completed in moderately with a small number of MR rounds. However, in the PPISEC algorithm, the centroid values are selected with the help of an improved support vector machine (ISVM) classifier. Thus, the incremental ensemble member chosen (IEMC) progression is performed with fuzzy-based firefly algorithm (FFA), and the normalised cut algorithm is established to accomplish high dimensional data clustering. The outcome shows that, the proposed PPISEC framework, which performs well on three benchmark samples by utilising high dimensionality and enhanced the results than the conventional clustering ensemble approaches.

Parallel progressive-based inductive subspace and fuzzy-based firefly algorithm for high ensemble data clustering

Parallel progressive-based inductive subspace and fuzzy-based firefly algorithm for high ensemble data clustering

Comparative analysis of selected algorithms for qualitative data clustering

Data clustering can apply to both numeric and categorical attributes. The numerical characteristics of data are all such values that can be written as digits. This work is not directly devoted to this category of attributes. The research's primary and most crucial goal is to compare the clustering processes, the quality of the obtained clusters, and the clustering results between the selected algorithms for clustering numerical sets (using coding techniques) and qualitative data sets. Design experiments were performed on three datasets. The algorithms used to group the observations into classes were the k-means algorithm and the kmodes algorithm. Three different distance determination measures were used in the case of the k-means algorithm - the Euclidean measure, the urban measure, and the Chebyshev measure. It can be concluded that for the structures of the three selected data sets, the k-modes algorithm presents better results of the clustering quality and thus more effectively distributes observations to classes. It also does it in a much shorter time than the k-means algorithm. The keyword used here, however, is dataset structure.